Rapid profiling of intact glucosinolates in Arabidopsis leaves by UHPLC-QTOFMS using a charged surface hybrid column.

INTRODUCTION: The analysis of glucosinolates (GS) is traditionally performed by reverse-phase liquid chromatography coupled to ultraviolet detection after a time-consuming desulphation step, which is required for increased retention. Simpler and more efficient alternative methods that can shorten both sample preparation and analysis are much needed. OBJECTIVE: To evaluate the feasibility of using ultrahigh-pressure liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UHPLC-QTOFMS) for the rapid profiling of intact GS. METHODOLOGY: A simple and short extraction of GS from Arabidopsis thaliana leaves was developed. Four sub-2 µm reverse-phase columns were tested for the rapid separation of these polar compounds using formic acid as the chromatographic additive. High-resolution QTOFMS was used to detect and identify GS. RESULTS: A novel charged surface hybrid (CSH) column was found to provide excellent retention and separation of GS within a total running time of 11 min. Twenty-one GS could be identified based on their accurate mass as well as isotopic and fragmentation patterns. The method was applied to determine the changes in GS content that occur after herbivory in Arabidopsis. In addition, we evaluated its applicability to the profiling of other Brassicaceae species. CONCLUSION: The method developed can profile the full range of GS, including the most polar ones, in a shorter time than previous methods, and is highly compatible with mass spectrometric detection.

Phylogenetic approach reveals that virus genotype largely determines HIV set-point viral load.

HIV virulence, i.e. the time of progression to AIDS, varies greatly among patients. As for other rapidly evolving pathogens of humans, it is difficult to know if this variance is controlled by the genotype of the host or that of the virus because the transmission chain is usually unknown. We apply the phylogenetic comparative approach (PCA) to estimate the heritability of a trait from one infection to the next, which indicates the control of the virus genotype over this trait. The idea is to use viral RNA sequences obtained from patients infected by HIV-1 subtype B to build a phylogeny, which approximately reflects the transmission chain. Heritability is measured statistically as the propensity for patients close in the phylogeny to exhibit similar infection trait values. The approach reveals that up to half of the variance in set-point viral load, a trait associated with virulence, can be heritable. Our estimate is significant and robust to noise in the phylogeny. We also check for the consistency of our approach by showing that a trait related to drug resistance is almost entirely heritable. Finally, we show the importance of taking into account the transmission chain when estimating correlations between infection traits. The fact that HIV virulence is, at least partially, heritable from one infection to the next has clinical and epidemiological implications. The difference between earlier studies and ours comes from the quality of our dataset and from the power of the PCA, which can be applied to large datasets and accounts for within-host evolution. The PCA opens new perspectives for approaches linking clinical data and evolutionary biology because it can be extended to study other traits or other infectious diseases.

Release of macrophage migration inhibitory factor by neuroendocrine-differentiated LNCaP cells sustains the proliferation and survival of prostate cancer cells.

The acquisition of neuroendocrine (NE) characteristics by prostate cancer (PCa) cells is closely related to tumour progression and hormone resistance. The mechanisms by which NE cells influence PCa growth and progression are not fully understood. Macrophage migration inhibitory factor (MIF) is a pro-inflammatory cytokine involved in oncogenic processes, and MIF serum levels correlate with aggressiveness of PCa. Here, we investigated the regulation and the functional consequences of MIF expression during NE transdifferentiation of PCa cells. NE differentiation (NED) of LNCaP cells, initiated either by increasing intracellular levels of cAMP or by culturing cells in an androgen-depleted medium, was associated with markedly increased MIF release. Yet, intracellular MIF protein and mRNA levels and MIF gene promoter activity decreased during NED of LNCaP cells, suggesting that NED favours MIF release despite decreasing MIF synthesis. Adenoviral-mediated forced MIF expression in NE-differentiated LNCaP cells increased cell proliferation without affecting the expression of NE markers. Addition of exogenous recombinant MIF to LNCaP and PC-3 cells stimulated the AKT and ERK1/2 signalling pathways, the expression of genes involved in PCa, as well as proliferation and resistance to paclitaxel and thapsigargin-induced apoptosis. Altogether, these data provide evidence that increased MIF release during NED in PCa may facilitate cancer progression or recurrence, especially following androgen deprivation. Thus, MIF could represent an attractive target for PCa therapy.

A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis.

Diagnosis of several neurological disorders is based on the detection of typical pathological patterns in the electroencephalogram (EEG). This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist in quantitative analysis and interpretation. We present a method, which allows automatic detection of epileptiform events and discrimination of them from eye blinks, and is based on features derived using a novel application of independent component analysis. The algorithm was trained and cross validated using seven EEGs with epileptiform activity. For epileptiform events with compensation for eyeblinks, the sensitivity was 65 +/- 22% at a specificity of 86 +/- 7% (mean +/- SD). With feature extraction by PCA or classification of raw data, specificity reduced to 76 and 74%, respectively, for the same sensitivity. On exactly the same data, the commercially available software Reveal had a maximum sensitivity of 30% and concurrent specificity of 77%. Our algorithm performed well at detecting epileptiform events in this preliminary test and offers a flexible tool that is intended to be generalized to the simultaneous classification of many waveforms in the EEG.

Plant traits co-vary with altitude in grasslands and forests in the European Alps

Biological traits that are advantageous under specific ecological conditions should be present in a large proportion of the species within an ecosystem, where those specific conditions prevail. As climatic conditions change, the frequency of certain traits in plant communities is expected to change with increasing altitude. We examined patterns of change for 13 traits in 120 exhaustive inventories of plants along five altitudinal transects (520-3100 m a.s.l.) in grasslands and in forests in western Switzerland. The traits selected for study represented the occupation of space, photosynthesis, reproduction and dispersal. For each plot, the mean trait values or the proportions of the trait states were weighted by species cover and examined in relation to the first axis of a PCA based on local climatic conditions. With increasing altitude in grasslands, we observed a decrease in anemophily and an increase in entomophily complemented by possible selfing; a decrease in diaspores with appendages adapted to ectozoochory, linked to a decrease in achenes and an increase in capsules. In lowlands, pollination and dispersal are ensured by wind and animals. However, with increasing altitude, insects are mostly responsible for pollination, and wind becomes the main natural dispersal vector. Some traits showed a particularly marked change in the alpine belt (e.g., the increase of capsules and the decrease of achenes), confirming that this belt concentrates particularly stressful conditions to plant growth and reproduction (e.g. cold, short growing season) that constrain plants to a limited number of strategies. One adaptation to this stress is to limit investment in dispersal by producing capsules with numerous, tiny seeds that have appendages limited to narrow wings. Forests displayed many of the trends observed in grasslands but with a reduced variability that is likely due to a shorter altitudinal gradient.

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest.

Functional connectivity (FC) as measured by correlation between fMRI BOLD time courses of distinct brain regions has revealed meaningful organization of spontaneous fluctuations in the resting brain. However, an increasing amount of evidence points to non-stationarity of FC; i.e., FC dynamically changes over time reflecting additional and rich information about brain organization, but representing new challenges for analysis and interpretation. Here, we propose a data-driven approach based on principal component analysis (PCA) to reveal hidden patterns of coherent FC dynamics across multiple subjects. We demonstrate the feasibility and relevance of this new approach by examining the differences in dynamic FC between 13 healthy control subjects and 15 minimally disabled relapse-remitting multiple sclerosis patients. We estimated whole-brain dynamic FC of regionally-averaged BOLD activity using sliding time windows. We then used PCA to identify FC patterns, termed "eigenconnectivities", that reflect meaningful patterns in FC fluctuations. We then assessed the contributions of these patterns to the dynamic FC at any given time point and identified a network of connections centered on the default-mode network with altered contribution in patients. Our results complement traditional stationary analyses, and reveal novel insights into brain connectivity dynamics and their modulation in a neurodegenerative disease.

Contrasting spatio-temporal climatic niche dynamics during the eastern and western invasions of spotted knapweed in North America

Aim The spotted knapweed (Centaurea stoebe), a plant native to south-east and central Europe, is highly invasive in North America. We investigated the spatio-temporal climatic niche dynamics of the spotted knapweed in North America along two putative eastern and western invasion routes. We then considered the patterns observed in the light of historical, ecological and evolutionary factors. Location Europe and North America. Methods The niche characteristics of the east and west invasive populations of spotted knapweed in North America were determined from documented occurrences over 120 consecutive years (1890-2010). The 2.5 and 97.5 percentiles of values along temperature and precipitation gradients, as given by the two first axes of a principal component axis (PCA), were then calculated. We additionally measured the climatic dissimilarity between invaded and native niches using a multivariate environmental similarity surface (MESS) analysis. Results Along both invasion routes, the species established in regions with climatic conditions that were similar to those in the native range in Europe. An initial spread in ruderal habitats always preceded spread in (semi-)natural habitats. In the east, the niche gradually increased over time until it reached limits similar to the native niche. Conversely, in the west the niche abruptly expanded after an extended time lag into climates not occupied in the native range; only the native cold niche limit was conserved. Main conclusions Our study reveals that different niche dynamics have taken place during the eastern and western invasions. This pattern indicates different combinations of historical, ecological and evolutionary factors in the two ranges. We hypothesize that the lack of a well-developed transportation network in the west at the time of the introduction of spotted knapweed confined the species to a geographically and climatically isolated region. The invasion of dry rangelands may have been favoured during the agricultural transition in the 1930s by release from natural enemies, local adaptation and less competitive vegetation, but further experimental and molecular studies are needed to explain these contrasting niche patterns fully. Our study illustrates the need and benefit of applying large-scale, temporally explicit approaches to understanding biological invasions.

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Objective: To assess the factorial validity of the Portuguese version of the Maslach Burnout Inventory - Human Services Survey (MBI-HSS). Methods: Between November 2010 and November 2011 a Portuguese version of the MBI-HSS was applied to 151 Portuguese family doctors (55% women, median age 54 years). The factorial structure of the MBI-HSS was examined by principal component analysis (PCA) and confirmatory factor analysis (CFA). Internal consistency estimates of the MBI-HSS were determined with Cronbach's alpha. Results: The fit of the hypothesized three-factor model to the data was superior to the alternative two-factor and four-factor models. CFA supported MBI-HSS as an acceptable measure to evaluate burnout and deletion of items 12 and 16 improved the goodness of fit of the model. In PCA, the three-factor model explained 50.58% of the variance and the four-factor model did not lead to understandable components. Item 12 was also found to be problematic in PCA. The Cronbach's alpha was satisfactory for emotional exhaustion (alpha=0.90), lack of personal accomplishment (alpha=0.73), and depersonalization (alpha=0.64). Conclusion: The Portuguese version of the MBI-HSS was found to be reliable to measure burnout among Portuguese medical doctors. We also recommend the deletion of items 12 and 16 from the MBI-HSS.

Survey on batch-to-batch variation in spray paints: a collaborative study

This study represents the most extensive analysis of batch-to-batch variations in spray paint samples to date. The survey was performed as a collaborative project of the ENFSI (European Network of Forensic Science Institutes) Paint and Glass Working Group (EPG) and involved 11 laboratories. Several studies have already shown that paint samples of similar color but from different manufacturers can usually be differentiated using an appropriate analytical sequence. The discrimination of paints from the same manufacturer and color (batch-to-batch variations) is of great interest and these data are seldom found in the literature. This survey concerns the analysis of batches from different color groups (white, papaya (special shade of orange), red and black) with a wide range of analytical techniques and leads to the following conclusions. Colored batch samples are more likely to be differentiated since their pigment composition is more complex (pigment mixtures, added pigments) and therefore subject to variations. These variations may occur during the paint production but may also occur when checking the paint shade in quality control processes. For these samples, techniques aimed at color/pigment(s) characterization (optical microscopy, microspectrophotometry (MSP), Raman spectroscopy) provide better discrimination than techniques aimed at the organic (binder) or inorganic composition (fourier transform infrared spectroscopy (FTIR) or elemental analysis (SEM - scanning electron microscopy and XRF - X-ray fluorescence)). White samples contain mainly titanium dioxide as a pigment and the main differentiation is based on the binder composition (Csingle bondH stretches) detected either by FTIR or Raman. The inorganic composition (elemental analysis) also provides some discrimination. Black samples contain mainly carbon black as a pigment and are problematic with most of the spectroscopic techniques. In this case, pyrolysis-GC/MS represents the best technique to detect differences. Globally, Py-GC/MS may show a high potential of discrimination on all samples but the results are highly dependent on the specific instrumental conditions used. Finally, the discrimination of samples when data was interpreted visually as compared to statistically using principal component analysis (PCA) yielded very similar results. PCA increases sensitivity and could perform better on specific samples, but one first has to ensure that all non-informative variation (baseline deviation) is eliminated by applying correct pre-treatments. Statistical treatments can be used on a large data set and, when combined with an expert's opinion, will provide more objective criteria for decision making.

Factor structure of the SOCRATES questionnaire in hospitalized medical patients.

The Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES), a 19-item instrument developed to assess readiness to change alcohol use among individuals presenting for specialized alcohol treatment, has been used in various populations and settings. Its factor structure and concurrent validity has been described for specialized alcohol treatment settings and primary care. The purpose of this study was to determine the factor structure and concurrent validity of the SOCRATES among medical inpatients with unhealthy alcohol use not seeking help for specialized alcohol treatment. The subjects were 337 medical inpatients with unhealthy alcohol use, identified during their hospital stay. Most of them had alcohol dependence (76%). We performed an Alpha Factor Analysis (AFA) and Principal Component Analysis (PCA) of the 19 SOCRATES items, and forced 3 factors and 2 components, in order to replicate findings from Miller and Tonigan (Miller, W. R., & Tonigan, J. S., (1996). Assessing drinkers' motivations for change: The Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES). Psychology of Addictive Behavior, 10, 81-89.) and Maisto et al. (Maisto, S. A., Conigliaro, J., McNeil, M., Kraemer, K., O'Connor, M., & Kelley, M. E., (1999). Factor structure of the SOCRATES in a sample of primary care patients. Addictive Behavior, 24(6), 879-892.). Our analysis supported the view that the 2 component solution proposed by Maisto et al. (Maisto, S.A., Conigliaro, J., McNeil, M., Kraemer, K., O'Connor, M., & Kelley, M.E., (1999). Factor structure of the SOCRATES in a sample of primary care patients. Addictive Behavior, 24(6), 879-892.) is more appropriate for our data than the 3 factor solution proposed by Miller and Tonigan (Miller, W. R., & Tonigan, J. S., (1996). Assessing drinkers' motivations for change: The Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES). Psychology of Addictive Behavior, 10, 81-89.). The first component measured Perception of Problems and was more strongly correlated with severity of alcohol-related consequences, presence of alcohol dependence, and alcohol consumption levels (average number of drinks per day and total number of binge drinking days over the past 30 days) compared to the second component measuring Taking Action. Our findings support the view that the SOCRATES is comprised of two important readiness constructs in general medical patients identified by screening.

Profiling of counterfeit medicines by vibrational spectroscopy

Counterfeit pharmaceutical products have become a widespread problem in the last decade. Various analytical techniques have been applied to discriminate between genuine and counterfeit products. Among these, Near-infrared (NIR) and Raman spectroscopy provided promising results.The present study offers a methodology allowing to provide more valuable information fororganisations engaged in the fight against counterfeiting of medicines.A database was established by analyzing counterfeits of a particular pharmaceutical product using Near-infrared (NIR) and Raman spectroscopy. Unsupervised chemometric techniques (i.e. principal component analysis - PCA and hierarchical cluster analysis - HCA) were implemented to identify the classes within the datasets. Gas Chromatography coupled to Mass Spectrometry (GC-MS) and Fourier Transform Infrared Spectroscopy (FT-IR) were used to determine the number of different chemical profiles within the counterfeits. A comparison with the classes established by NIR and Raman spectroscopy allowed to evaluate the discriminating power provided by these techniques. Supervised classifiers (i.e. k-Nearest Neighbors, Partial Least Squares Discriminant Analysis, Probabilistic Neural Networks and Counterpropagation Artificial Neural Networks) were applied on the acquired NIR and Raman spectra and the results were compared to the ones provided by the unsupervised classifiers.The retained strategy for routine applications, founded on the classes identified by NIR and Raman spectroscopy, uses a classification algorithm based on distance measures and Receiver Operating Characteristics (ROC) curves. The model is able to compare the spectrum of a new counterfeit with that of previously analyzed products and to determine if a new specimen belongs to one of the existing classes, consequently allowing to establish a link with other counterfeits of the database.

p53 and cyclooxygenase-2 expression are directly associated with cyclin D1 expression in radical prostatectomy specimens of patients with hormone-naïve prostate cancer.

Prostate cancer (PCa) is a potentially curable disease when diagnosed in early stages and subsequently treated with radical prostatectomy (RP). However, a significant proportion of patients tend to relapse early, with the emergence of biochemical failure (BF) as an established precursor of progression to metastatic disease. Several candidate molecular markers have been studied in an effort to enhance the accuracy of existing predictive tools regarding the risk of BF after RP. We studied the immunohistochemical expression of p53, cyclooxygenase-2 (COX-2) and cyclin D1 in a cohort of 70 patients that underwent RP for early stage, hormone naïve PCa, with the aim of prospectively identifying any possible interrelations as well as correlations with known prognostic parameters such as Gleason score, pathological stage and time to prostate-specific antigen (PSA) relapse. We observed a significant (p = 0.003) prognostic role of p53, with high protein expression correlating with shorter time to BF (TTBF) in univariate analysis. Both p53 and COX-2 expression were directly associated with cyclin D1 expression (p = 0.055 and p = 0.050 respectively). High p53 expression was also found to be an independent prognostic factor (p = 0.023). Based on previous data and results provided by this study, p53 expression exerts an independent negative prognostic role in localized prostate cancer and could therefore be evaluated as a useful new molecular marker to be added in the set of known prognostic indicators of the disease. With respect to COX-2 and cyclin D1, further studies are required to elucidate their role in early prediction of PCa relapse after RP.

Isolation and characterization of carcinoembryonic antigen (CEA) extracted from normal human colon mucosa.

Carcinoembryonic antigen (CEA) was identified in perchloric acid (PCA)_extract from normal colon mucosa by 2 immunological criteria: a line of identity in double diffusion and a parallel inhibition curve in radioimmunoassay (RIA), both with reference colon carcinoma-CEA (CEA-Tu). The average concentration of CEA in normal colon mucosa (CEA-No) was 35 times lower than in primary large bowel carcinomas and 230 times lower than in metastatic colon or rectum carcinomas. CEA-No was purified from PCA extracts of normal colon mucosa by Sephadex G-200 filtration and immunoadsorbent columns. Purified CEA-No had quatitatively the same inhibition activity in RIA as the British Standard CEA coded 73/601. Purified CEA-No was labelled with 125I. The percentage of binding of labelled CEA-No to a specific goat anti-CEA-Tu antiserum was similar to that of CEA-Tu. Labelled CEA-No could be used as radioactive tracer in RIA as well as labelled CEA-Tu. The physico-chemical properties of purified CEA-Tu as demonstrated by Sepharose 6 B filtration, SDS Polyacrylamide gel analysis and cesium chloride density gradient, were found to be almost identical to those of reference CEA-Tu. Preliminary results showed that CEA-No and CEA-Tu contained the same types of carbohydrates in similar proportions. A rabbit antiserum against CEA-No was obtained which demonstrated the same specificity as conventional anti-CEA-Tu antisera.

Breakpoints in ventilation, cerebral and muscle oxygenation, and muscle activity during an incremental cycling exercise.

The aim of this study was to locate the breakpoints of cerebral and muscle oxygenation and muscle electrical activity during a ramp exercise in reference to the first and second ventilatory thresholds. Twenty-five cyclists completed a maximal ramp test on an electromagnetically braked cycle-ergometer with a rate of increment of 25 W/min. Expired gazes (breath-by-breath), prefrontal cortex and vastus lateralis (VL) oxygenation [Near-infrared spectroscopy (NIRS)] together with electromyographic (EMG) Root Mean Square (RMS) activity for the VL, rectus femoris (RF), and biceps femoris (BF) muscles were continuously assessed. There was a non-linear increase in both cerebral deoxyhemoglobin (at 56 ± 13% of the exercise) and oxyhemoglobin (56 ± 8% of exercise) concomitantly to the first ventilatory threshold (57 ± 6% of exercise, p > 0.86, Cohen's d < 0.1). Cerebral deoxyhemoglobin further increased (87 ± 10% of exercise) while oxyhemoglobin reached a plateau/decreased (86 ± 8% of exercise) after the second ventilatory threshold (81 ± 6% of exercise, p < 0.05, d > 0.8). We identified one threshold only for muscle parameters with a non-linear decrease in muscle oxyhemoglobin (78 ± 9% of exercise), attenuation in muscle deoxyhemoglobin (80 ± 8% of exercise), and increase in EMG activity of VL (89 ± 5% of exercise), RF (82 ± 14% of exercise), and BF (85 ± 9% of exercise). The thresholds in BF and VL EMG activity occurred after the second ventilatory threshold (p < 0.05, d > 0.6). Our results suggest that the metabolic and ventilatory events characterizing this latter cardiopulmonary threshold may affect both cerebral and muscle oxygenation levels, and in turn, muscle recruitment responses.

Mult-class differentiation of cannabis seedlings in a forensic context

This article presents an experimental study about the classification ability of several classifiers for multi-classclassification of cannabis seedlings. As the cultivation of drug type cannabis is forbidden in Switzerland lawenforcement authorities regularly ask forensic laboratories to determinate the chemotype of a seized cannabisplant and then to conclude if the plantation is legal or not. This classification is mainly performed when theplant is mature as required by the EU official protocol and then the classification of cannabis seedlings is a timeconsuming and costly procedure. A previous study made by the authors has investigated this problematic [1]and showed that it is possible to differentiate between drug type (illegal) and fibre type (legal) cannabis at anearly stage of growth using gas chromatography interfaced with mass spectrometry (GC-MS) based on therelative proportions of eight major leaf compounds. The aims of the present work are on one hand to continueformer work and to optimize the methodology for the discrimination of drug- and fibre type cannabisdeveloped in the previous study and on the other hand to investigate the possibility to predict illegal cannabisvarieties. Seven classifiers for differentiating between cannabis seedlings are evaluated in this paper, namelyLinear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), Nearest NeighbourClassification (NNC), Learning Vector Quantization (LVQ), Radial Basis Function Support Vector Machines(RBF SVMs), Random Forest (RF) and Artificial Neural Networks (ANN). The performance of each method wasassessed using the same analytical dataset that consists of 861 samples split into drug- and fibre type cannabiswith drug type cannabis being made up of 12 varieties (i.e. 12 classes). The results show that linear classifiersare not able to manage the distribution of classes in which some overlap areas exist for both classificationproblems. Unlike linear classifiers, NNC and RBF SVMs best differentiate cannabis samples both for 2-class and12-class classifications with average classification results up to 99% and 98%, respectively. Furthermore, RBFSVMs correctly classified into drug type cannabis the independent validation set, which consists of cannabisplants coming from police seizures. In forensic case work this study shows that the discrimination betweencannabis samples at an early stage of growth is possible with fairly high classification performance fordiscriminating between cannabis chemotypes or between drug type cannabis varieties.