Bottom-up effects of climate on fish populations: data from the Continuous Plankton Recorder

The Continuous Plankton Recorder (CPR) dataset on fish larvae has an extensive spatio-temporal coverage that allows the responses of fish populations to past changes in climate variability, including abrupt changes such as regime shifts, to be investigated. The newly available dataset offers a unique opportunity to investigate long-term changes over decadal scales in the abundance and distribution of fish larvae in relation to physical and biological factors. A principal component analysis (PCA) using 7 biotic and abiotic parameters is applied to investigate the impact of environmental changes in the North Sea on 5 selected taxa of fish larvae during the period 1960 to 2004. The analysis revealed 4 periods of time (1960–1976; 1977–1982; 1983–1996; 1997–2004) reflecting 3 different ecosystem states. The larvae of clupeids, sandeels, dab and gadoids seemed to be affected mainly by changes in the plankton ecosystem, while the larvae of migratory species such as Atlantic mackerel responded more to hydrographic changes. Climate variability seems more likely to influence fish populations through bottom-up control via a cascading effect from changes in the North Atlantic Oscillation (NAO) impacting on the hydro dynamic features of the North Sea, in turn impacting on the plankton available as prey for fish larvae. The responses and adaptability of fish larvae to changing environmental conditions, parti cularly to changes in prey availability, are complex and species-specific. This complexity is enhanced with fishing effects interacting with climate effects and this study supports furthering our under - standing of such interactions before attempting to predict how fish populations respond to climate variability

Heavy metal accumulation reflecting natural sedimentary processes and anthropogenic activities in two contrasting coastal wetland ecosystems, eastern China

Due to the impacts of natural processes and anthropogenic activities, different coastal wetlands are faced with variable patterns of heavy metal contamination. It is important to quantify the contributions of pollutant sources, in order to adopt appropriate protection measures for local ecosystems. The aim of this research was to compare the heavy metal contamination patterns of two contrasting coastal wetlands in eastern China. In addition, the contributions from various metal sources were identified and quantified, and influencing factors, such as the role of the plant Spartina alterniflora, were evaluated. Materials and methods Sediment samples were taken from two coastal wetlands (plain-type tidal flat at the Rudong (RD) wetland vs embayment-type tidal flat at Luoyuan Bay (LY)) to measure the content of Al, Fe, Co, Cr, Cu, Mn, Mo, Ni, Sr, Zn, Pb, Cd, and As. Inductively coupled plasma atomic emission spectrometry, flame atomic absorption spectrometry, and atomic fluorescence spectrometry methods were used for metal detection. Meanwhile, the enrichment factor and geoaccumulation index were applied to assess the pollution level. Principle component analysis and receptor modeling were used to quantify the sources of heavy metals. Results and discussion Marked differences in metal distribution patterns between the two systems were present. Metal contents in LY were higher than those in RD, except for Sr and Mo. The growth status of S. alterniflora influenced metal accumulations in RD, i.e., heavy metals were more easily adsorbed in the sediment in the following sequence: Cu > Cd > Zn > Cr > Al > Pb ≥ Ni ≥ Co > Fe > Sr ≥ Mn > As > Mo as a result of the presence and size of the vegetation. However, this phenomenon was not observed in LY. A higher potential ecological risk was associated with LY, compared with RD, except for Mo. Based on a receptor model output, sedimentary heavy metal contents at RD were jointly influenced by natural sedimentary processes and anthropogenic activities, whereas they were dominated by anthropogenic activities at LY. Conclusions A combination of geochemical analysis and modeling approaches was used to quantify the different types of natural and anthropogenic contributions to heavy metal contamination, which is useful for pollution assessments. The application of this approach reveals that natural and anthropogenic processes have different influences on the delivery and retention of metals at the two contrasting coastal wetlands. In addition, the presence and size of S. alterniflora can influence the level of metal contamination in sedimentary environments.

Biomonitoring of air pollutants by using lichens (Evernia prunastri) in areas between Kenitra and Mohammedia cities in Morocco.

In this study, Evernia prunastri, a lichen growing in its natural habitat in Morocco was analysed for the concentration of five heavy metals (Fe, Pb, Zn, Cu and Cr) from eleven sites between Kenitra and Mohammedia cities. The control site was Dar Essalam, an isolated area with low traffic density and dense vegetation. In the investigated areas, the concentration of heavy metals was correlated with vehicular traffic, industrial activity and urbanization. The total metal concentration was highest in Sidi Yahya, followed by Mohammedia and Bouznika. The coefficient of variation was higher for Pb and lower for Cu, Zn and Fe. The concentrations of most heavy metals in the thalli differed significantly between sites (p<0.01). Principal component analysis (PCA) revealed a significant correlation between heavy metal accumulation and atmospheric purity index. This study demonstrated also that the factors most strongly affecting the lichen flora were traffic density, the petroleum industry and paper factories in these areas. Overall, these results suggest that the index of atmospheric purity and assessment of heavy metals in lichen thalli are good indicators of the air quality at the studied sites.

Fault Diagnosis in Internal Combustion Engines Using Nonlinear Multivariate Statistics

This paper presents a statistical-based fault diagnosis scheme for application to internal combustion engines. The scheme relies on an identified model that describes the relationships between a set of recorded engine variables using principal component analysis (PCA). Since combustion cycles are complex in nature and produce nonlinear relationships between the recorded engine variables, the paper proposes the use of nonlinear PCA (NLPCA). The paper further justifies the use of NLPCA by comparing the model accuracy of the NLPCA model with that of a linear PCA model. A new nonlinear variable reconstruction algorithm and bivariate scatter plots are proposed for fault isolation, following the application of NLPCA. The proposed technique allows the diagnosis of different fault types under steady-state operating conditions. More precisely, nonlinear variable reconstruction can remove the fault signature from the recorded engine data, which allows the identification and isolation of the root cause of abnormal engine behaviour. The paper shows that this can lead to (i) an enhanced identification of potential root causes of abnormal events and (ii) the masking of faulty sensor readings. The effectiveness of the enhanced NLPCA based monitoring scheme is illustrated by its application to a sensor fault and a process fault. The sensor fault relates to a drift in the fuel flow reading, whilst the process fault relates to a partial blockage of the intercooler. These faults are introduced to a Volkswagen TDI 1.9 Litre diesel engine mounted on an experimental engine test bench facility.

Effect of excitation wavelength on the Raman spectroscopy of the porcine photoreceptor layer from the area centralis.

Raman microscopy, based upon the inelastic scattering (Raman) of light by molecular species, has been applied as a specific structural probe in a wide range of biomedical samples. The purpose of the present investigation was to assess the potential of the technique for spectral characterization of the porcine outer retina derived from the area centralis, which contains the highest proportion of cone:rod cell ratio in the pig retina. METHODS: Retinal cross-sections, immersion-fixed in 4% (w/v) PFA and cryoprotected, were placed on salinized slides and air-dried prior to direct Raman microscopic analysis at three excitation wavelengths, 785 nm, 633 nm, and 514 nm. RESULTS: Raman spectra of each of the photoreceptor inner and outer segments (PIS, POS) and of the outer nuclear layer (ONL) of the retina acquired at 785 nm were dominated by vibrational features characteristic of proteins and lipids. There was a clear difference between the inner and outer domains in the spectroscopic regions, amide I and III, known to be sensitive to protein conformation. The spectra recorded with 633 nm excitation mirrored those observed at 785 nm excitation for the amide I region, but with an additional pattern of bands in the spectra of the PIS region, attributed to cytochrome c. The same features were even more enhanced in spectra recorded with 514 nm excitation. A significant nucleotide contribution was observed in the spectra recorded for the ONL at all three excitation wavelengths. A Raman map was constructed of the major spectral components found in the retinal outer segments, as predicted by principal component analysis of the data acquired using 633 nm excitation. Comparison of the Raman map with its histological counterpart revealed a strong correlation between the two images. CONCLUSIONS: It has been demonstrated that Raman spectroscopy offers a unique insight into the biochemical composition of the light-sensing cells of the retina following the application of standard histological protocols. The present study points to the considerable promise of Raman microscopy as a component-specific probe of retinal tissue.

Dynamic Multivariate Statistical Process Control using Subspace Identification

This paper points out a serious flaw in dynamic multivariate statistical process control (MSPC). The principal component analysis of a linear time series model that is employed to capture auto- and cross-correlation in recorded data may produce a considerable number of variables to be analysed. To give a dynamic representation of the data (based on variable correlation) and circumvent the production of a large time-series structure, a linear state space model is used here instead. The paper demonstrates that incorporating a state space model, the number of variables to be analysed dynamically can be considerably reduced, compared to conventional dynamic MSPC techniques.

Differences In Metabolic Profile In the Testes of Streptozotocin Induced Diabetes Mellitus Mice As Detected By 1H NMR

Contrary to the traditional view, recent studies suggest that diabetes mellitus has an adverse influence on male reproductive function. Our aim was to determine the affect of diabetes on the testicular environment by identifying and then assessing perturbations in small molecule metabolites. Testes were obtained from control and streptozotocin induced diabetic C57BL/6 mice, two, four and eight weeks post treatment. Diabetic status was confirmed by HbA1c, non fasting blood glucose, physiological condition and body weight. Protein free, low molecular weight, water soluble extracts were assessed using 1H NMR spectroscopy. Principal Component Analysis of the derived profiles was used to classify any variations and specific metabolites were identified based on their spectral pattern. Characteristic metabolite profiles were identified for control and diabetic animals with the most distinctive being from mice with the greatest physical deterioration and loss of bodyweight. Eight streptozotocin treated animals did not develop diabetes and displayed profiles similar to controls. Diabetic mice had decreases in creatine, choline and carnitine and increases in lactate, alanine and myo-inositol. Betaine levels were found to be increased in the majority of diabetic mice but decreased in two animals with severe loss of body weight and physical condition. The association between perturbations in a number of small molecule metabolites known to be influential in sperm function, with diabetic status and physiological condition, adds further impetus to the proposal that diabetes influences important spermatogenic pathways and mechanisms in a subtle and previously unrecognised manner.

Prediction of adipose tissue composition using Raman spectroscopy: average properties and individual fatty acids.

Abstract: Raman spectroscopy has been used for the first time to predict the FA composition of unextracted adipose tissue of pork, beef, lamb, and chicken. It was found that the bulk unsaturation parameters could be predicted successfully [R-2 = 0.97, root mean square error of prediction (RMSEP) = 4.6% of 4 sigma], with cis unsaturation, which accounted for the majority of the unsaturation, giving similar correlations. The combined abundance of all measured PUFA (>= 2 double bonds per chain) was also well predicted with R-2 = 0.97 and RMSEP = 4.0% of 4 sigma. Trans unsaturation was not as well modeled (R-2 = 0.52, RMSEP = 18% of 4 sigma); this reduced prediction ability can be attributed to the low levels of trans FA found in adipose tissue (0.035 times the cis unsaturation level). For the individual FA, the average partial least squares (PLS) regression coefficient of the 18 most abundant FA (relative abundances ranging from 0.1 to 38.6% of the total FA content) was R-2 = 0.73; the average RMSEP = 11.9% of 4 sigma. Regression coefficients and prediction errors for the five most abundant FA were all better than the average value (in some cases as low as RMSEP = 4.7% of 4 sigma). Cross-correlation between the abundances of the minor FA and more abundant acids could be determined by principal component analysis methods, and the resulting groups of correlated compounds were also well-predicted using PLS. The accuracy of the prediction of individual FA was at least as good as other spectroscopic methods, and the extremely straightforward sampling method meant that very rapid analysis of samples at ambient temperature was easily achieved. This work shows that Raman profiling of hundreds of samples per day is easily achievable with an automated sampling system.

Fault reconstruction in linear dynamic systems using multivariate statistics

Treasure et al. (2004) recently proposed a new sub space-monitoring technique, based on the N4SID algorithm, within the multivariate statistical process control framework. This dynamic-monitoring method requires considerably fewer variables to be analysed when compared with dynamic principal component analysis (PCA). The contribution charts and variable reconstruction, traditionally employed for static PCA, are analysed in a dynamic context. The contribution charts and variable reconstruction may be affected by the ratio of the number of retained components to the total number of analysed variables. Particular problems arise if this ratio is large and a new reconstruction chart is introduced to overcome these. The utility of such a dynamic contribution chart and variable reconstruction is shown in a simulation and by application to industrial data from a distillation unit.

Improved reliability in diagnosing faults using multivariate statistics

This paper analyses multivariate statistical techniques for identifying and isolating abnormal process behaviour. These techniques include contribution charts and variable reconstructions that relate to the application of principal component analysis (PCA). The analysis reveals firstly that contribution charts produce variable contributions which are linearly dependent and may lead to an incorrect diagnosis, if the number of principal components retained is close to the number of recorded process variables. The analysis secondly yields that variable reconstruction affects the geometry of the PCA decomposition. The paper further introduces an improved variable reconstruction method for identifying multiple sensor and process faults and for isolating their influence upon the recorded process variables. It is shown that this can accommodate the effect of reconstruction, i.e. changes in the covariance matrix of the sensor readings and correctly re-defining the PCA-based monitoring statistics and their confidence limits. (c) 2006 Elsevier Ltd. All rights reserved.

Process monitoring approach using fast moving window PCA

This paper introduces a fast algorithm for moving window principal component analysis (MWPCA) which will adapt a principal component model. This incorporates the concept of recursive adaptation within a moving window to (i) adapt the mean and variance of the process variables, (ii) adapt the correlation matrix, and (iii) adjust the PCA model by recomputing the decomposition. This paper shows that the new algorithm is computationally faster than conventional moving window techniques, if the window size exceeds 3 times the number of variables, and is not affected by the window size. A further contribution is the introduction of an N-step-ahead horizon into the process monitoring. This implies that the PCA model, identified N-steps earlier, is used to analyze the current observation. For monitoring complex chemical systems, this work shows that the use of the horizon improves the ability to detect slowly developing drifts.

Factors influencing the distribution of native and introduced Gammarus spp. in Irish river systems

Populations of Gammarus duebeni celticus, previously the only amphipod species resident in the rivers of the Lough Neagh catchment, N. Ireland, have been subjected to invasion by G. pulex from the British mainland. Numerous previous studies have investigated the potential behavioural mechanisms, principally differential mutual predation, underlying the replacement of G. d. celticus by G. pulex in Irish waters, and the mutually exclusive distributions of these species in Britain and mainland Europe. However, the relative degree of influence of abiotic versus biotic factors in structuring these amphipod communities remains unresolved. This study used principal component analysis (PCA) to distinguish physico-chemical parameters that have significant roles in determining the current distribution of G. pulex relative to G. d. celticus in L. Neagh rivers. We show that the original domination of rivers by the native G. d, celticus has changed radically, with many sites in several rivers containing either both species or only G. pulex. G. pulex was more abundant than the G. d. celticus in sites with low dissolved oxygen levels. This was reflected in the macroinvertebrate assemblages associated with G. pulex in these sites, which tended to be those tolerant of low biological water quality. The present study thus emphasizes the importance of the habitat template, particularly water quality, for Gammarus spp. interactions. If rivers become increasingly stressed by organic pollution, it is probable the range expansion of G. pulex will continue. Because these two species are not ecological equivalents, the outcomes of G. pulex incursions into G. d. celticus sites may ultimately depend on the prevailing physico-chemical regimes in each site.

Improved fault diagnosis in multivariate systems using regression-based reconstruction

Subspace monitoring has recently been proposed as a condition monitoring tool that requires considerably fewer variables to be analysed compared to dynamic principal component analysis (PCA). This paper analyses subspace monitoring in identifying and isolating fault conditions, which reveals that the existing work suffers from inherent limitations if complex fault senarios arise. Based on the assumption that the fault signature is deterministic while the monitored variables are stochastic, the paper introduces a regression-based reconstruction technique to overcome these limitations. The utility of the proposed fault identification and isolation method is shown using a simulation example and the analysis of experimental data from an industrial reactive distillation unit.

Application of NMR based metabolomics for mapping metabolite variation in European wheat

In this study, we report on the use of NMR-based metabolomics to access variation in low molecular weight polar metabolites between the European wheat cultivars Apache, Charger, Claire and Orvantis. Previous unassigned resonances in the published NMR spectra of wheat extracts were identified using C NMR and two dimensional proton-carbon NMR. These included a peak for trans-aconitate (d3.43) and resonances corresponding to fructose in the crowded carbohydrate region of the spectra. Large metabolite differences were observed between two different growth stages, namely the coleoptile and two week old leaf tissue extracts which were consistent across cultivars. Two week old leaf tissue extracts had higher abundances of glutamine, glutamate, sucrose and trans-aconitate and less glucose and fructose than were observed in the coleoptile extracts. Across both growth stages the cultivars Apache and Charger showed the greatest differences in metabolite profiles. Charger had higher abundances of betaine, the single most influential metabolite in the principal component analysis, in addition to fructose and sucrose. However, Charger had lower levels of aspartate, choline and glucose than Apache. These findings demonstrate the potential for a biochemical mapping approach using NMR, across European wheat germplasm, for metabolites of known importance to functional characteristics. © Springer Science+Business Media, LLC 2009.

Ex vivo diagnosis of lung cancer using a Raman miniprobe.

Lung cancer is the most common cause of cancer death. The conventional method of confirming the diagnosis is bronchoscopy, inspecting the airways of the patient with a fiber optic endoscope. A number of studies have shown that Raman spectroscopy can diagnose lung cancer in vitro. In this study, Raman spectra were obtained from ex vivo normal and malignant lung tissue using a minifiber optic Raman probe suitable for insertion into the working channel of a bronchoscope. Shifted subtracted Raman spectroscopy was used to reduce the fluorescence from the lung tissue. Using principal component analysis with a leave-one-out analysis, the tissues were classified accurately. This novel technique has the potential to obtain Raman spectra from tumors from patients with lung cancer in vivo.