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.

Analysis of Extended Partial Least Squares for Monitoring Large-Scale Processes

This paper theoretically analysis the recently proposed "Extended Partial Least Squares" (EPLS) algorithm. After pointing out some conceptual deficiencies, a revised algorithm is introduced that covers the middle ground between Partial Least Squares and Principal Component Analysis. It maximises a covariance criterion between a cause and an effect variable set (partial least squares) and allows a complete reconstruction of the recorded data (principal component analysis). The new and conceptually simpler EPLS algorithm has successfully been applied in detecting and diagnosing various fault conditions, where the original EPLS algorithm did only offer fault detection.

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.

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.

Coastline configuration as a determinant of structure in larval assemblages

The richness and turnover of coastal larval pools set upper limits for biodiversity in coastal systems. For particular local systems, such as embayments, the characteristics of the local larval pool are determined by the relative contributions of locally produced and external larvae. The balance between these sources partially reflects the extent of tidal exchange and is hence related to system size and flushing time. Larvae of benthic marine invertebrates were sampled from 8 bays along the Irish coast to investigate the effect of coastline configuration on the characteristics of the larval pool. Flushing time explained 34.5% of the variability in species richness from a series of daily samples. Many of the potentially relevant environmental variables are correlated, limiting the potential for individual variables to be examined in isolation. We therefore used a principal components analysis to describe the major patterns in environmental variability across bays. The second principal component separated bays along a gradient of increasing depth, salinity, tidal range and flushing time. Scores along this component were generally better predictors of the larval pool than single variables, explaining as much as 61.2% of the variation in species richness, diversity and similarity between dates. Deeper bays, with more saline water and longer flushing times, tended to have richer and more diverse larval pools, with a greater consistency in species composition between sample dates. No relationship was found between environmental variables and larval abundance. Our results suggest that flushing time, particularly when in combination with topographic variables, chlorophyll, tidal range and salinity, may be a useful predictor for the richness and turnover of local larval pools.

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.

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.

Responses of calves to acute stress: Individual consistency and relations between behavioral and physiological measures

The present study examined the consistency over time of individual differences in behavioral and physiological responsiveness of calves to intuitively alarming test situations as well as the relationships between behavioral and physiological measures. Twenty Holstein Friesian heifer calves were individually subjected to the same series of two behavioral and two hypothalamo-pituitary-adrenocortical (HPA) axis reactivity tests at 3, 13 and 26 weeks of age. Novel environment (open field, OF) and novel object (NO) tests involved measurement of behavioral, plasma cortisol and heart rate responses. Plasma ACTH and/or cortisol response profiles were determined after administration of exogenous CRH and ACTH, respectively, in the HPA axis reactivity tests. Principal component analysis (PCA) was used to condense correlated measures within ages into principal components reflecting independent dimensions underlying the calves' reactivity. Cortisol responses to the OF and NO tests were positively associated with the latency to contact and negatively related to the time spent in contact with the NO. Individual differences in scores of a principal component summarizing this pattern of inter-correlations, as well as differences in separate measures of adrenocortical and behavioral reactivity in the OF and NO tests proved highly consistent over time. The cardiac response to confinement in a start box prior to the OF test was positively associated with the cortisol responses to the OF and NO tests at 26 weeks of age. HPA axis reactivity to ACTH or CRH was unrelated to adrenocortical and behavioral responses to novelty. These findings strongly suggest that the responsiveness of calves was mediated by stable individual characteristics. Correlated adrenocortical and behavioral responses to novelty may reflect underlying fearfulness, defining the individual's susceptibility to the elicitation of fear. Other independent characteristics mediating reactivity may include activity or coping style (related to locomotion) and underlying sociality (associated with vocalization). (c) 2005 Elsevier Inc. All rights reserved.

Raman microscopy in the diagnosis and prognosis of surgically resected nonsmall cell lung cancer

The main curative therapy for patients with nonsmall cell lung cancer is surgery. Despite this, the survival rate is only 50%, therefore it is important to more efficiently diagnose and predict prognosis for lung cancer patients. Raman spectroscopy is useful in the diagnosis of malignant and premalignant lesions. The aim of this study is to investigate the ability of Raman microscopy to diagnose lung cancer from surgically resected tissue sections, and predict the prognosis of these patients. Tumor tissue sections from curative resections are mapped by Raman microscopy and the spectra analzsed using multivariate techniques. Spectra from the tumor samples are also compared with their outcome data to define their prognostic significance. Using principal component analysis and random forest classification, Raman microscopy differentiates malignant from normal lung tissue. Principal component analysis of 34 tumor spectra predicts early postoperative cancer recurrence with a sensitivity of 73% and specificity of 74%. Spectral analysis reveals elevated porphyrin levels in the normal samples and more DNA in the tumor samples. Raman microscopy can be a useful technique for the diagnosis and prognosis of lung cancer patients receiving surgery, and for elucidating the biochemical properties of lung tumors. (C) 2010 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3323088]

Cannabinoid Receptor influences Chromatin Remodelling in Mouse Spermatids by affecting Content of Transition Protein 2 mRNA and Histone Replacement

Marijuana smokers and animals treated with ?9-tetrahydrocannabinol, THC, the principal component of marijuana, show alterations of sperm morphology suggesting a role for cannabinoids in sperm differentiation and/or maturation. Since the cannabinoid receptor 1 (CNR1) activation appears to play a pivotal role in spermiogenesis, the developmental stage where DNA is remodeled, we hypothesized that CNR1 receptors might also influence chromatin quality in sperm. We used Cnr1 null mutant (Cnr1-/-) mice to study the possible role of endocannabinoids on sperm chromatin during spermiogenesis. We demonstrated that CNR1 activation regulated chromatin remodeling of spermatids by either increasing Tnp2 levels or enhancing histone displacement. Comparative analysis of WT, Cnr1+/- and Cnr1-/- animals suggested the possible occurrence of haploinsufficiency for Tnp2 turnover control by CNR1, while histone displacement was disrupted to a lesser extent. Further, flow cytometry analysis demonstrated that the genetic loss of Cnr1 decreased sperm chromatin quality and was associated with sperm DNA fragmentation. This damage increased during epididymal transit, from caput to cauda. Collectively, our results show that the expression/activity of CNR1 controls the physiological alterations of DNA structure during spermiogenic maturation and epididymal transit. Given the deleterious effects of sperm DNA damage on male fertility, we suggest that the reproductive function of marijuana users may also be impaired by deregulation of the endogenous endocannabinoid system.

Statistical-based monitoring of multivariate non-Gaussian systems

The monitoring of multivariate systems that exhibit non-Gaussian behavior is addressed. Existing work advocates the use of independent component analysis (ICA) to extract the underlying non-Gaussian data structure. Since some of the source signals may be Gaussian, the use of principal component analysis (PCA) is proposed to capture the Gaussian and non-Gaussian source signals. A subsequent application of ICA then allows the extraction of non-Gaussian components from the retained principal components (PCs). A further contribution is the utilization of a support vector data description to determine a confidence limit for the non-Gaussian components. Finally, a statistical test is developed for determining how many non-Gaussian components are encapsulated within the retained PCs, and associated monitoring statistics are defined. The utility of the proposed scheme is demonstrated by a simulation example, and the analysis of recorded data from an industrial melter.