Antibodies with broad specificity to azaspiracids by use of synthetic haptens

The development of general, sensitive, portable, and quantitative assays for the azaspiracid (AZA) class of marine toxins is urgently needed. Use of a synthetic hapten containing rings F-I of AZA to generate antibodies that cross-react with the AZAs via their common C28-C40 domain and use of these antibodies in ELISA and immunoaffinity columns are reported. This approach has many advantages over using intact azaspiracids (AZAs) derived from environmental samples or total synthesis as haptens for antibody development. A derivative of the levorotatory C28-C40 azaspiracid domain (1) was synthesized efficiently using a one-pot Staudinger reduction/intramolecular aza-Wittig reaction-imine capture sequence to form the H-I ring spiroaminal and a double intramolecluar hetero-Michael addition to assemble the F-G ring ketal. Conjugation of the hapten 1 to cBSA and immunization in sheep generated antibodies that recognized and bound to ovalbumin-conjugated 1 in the absence of AZA1. This binding was inhibited by 1 in a concentration-dependent manner. A mixture of AZA1, AZA2, AZA3, and AZA6 caused a degree of inhibition of antibody binding consistent with its total AZA content, rather than just its content of AZA1. This result suggests that the antibodies also have a similar affinity for AZA2, AZA3, and AZA6 as they do for AZA1 and that such antibodies are suitable for analysis of AZAs in shellfish samples.     

Compressibility study of quaternary phospholipid blend monolayers

The mechanical properties of liposome membranes are strongly dependent on type and ratio of lipid compounds, which can have important role in drug targeting and release processes when liposome is used as drug carrier. In this work we have used Brewster's angle microscopy to monitor the lateral compression process of lipid monolayers containing as helper lipids either distearoyl phosphatidylethanolamine (DSPE) or dioleoyl phophatidylethanolamine (DOPE) molecules on the Langmuir trough. The compressibility coefficient was determined for lipid blend monolayers containing the helper lipids above, cholesterol, distearoyl phosphatidylcholine (DSPC) and pegylated-DSPE at room temperature. Two variables, the cholesterol fraction and the ratio ρ between the helper lipid (either DSPE or DOPE) and the reference lipid DSPC, were studied by multivariate analysis to evaluate their impact on the compressibility coefficient of the monolayers. The cholesterol level was found to be the most significant variable for DSPE blends while the ratio ρ was the most significant one for DOPE blend monolayers. It was also found that these two variables can exhibit positive interaction and the same compressibility value can be obtained with different blend compositions.     

Application of simca multivariate data analysis to the classification of gas chromatographic profiles of human brain tissues

Sixteen samples of three types (classes) of brain tissue were characterized by capillary gas chromatography (g.c.). Each sample is thus characterized by the peak heights of 105 peaks in each g.c. profile. SIMCA pattern recognition is used to analyze the 16 × 105 data matrix in order to differentiate between the three classes on the basis of the g.c. data only. The SIMCA method is therefore applicable even when the number of variables (105) exceeds the number of objects (16). The results indicate that g.c. profiles are useful for the identification of brain tissue type.     

Estimating Response Ratios from Continuous Outcome Data

Several methods for imputing the number of responders from summary continuous outcome data in randomized controlled trials exist. A method by Furukawa and others was used in the quite common case that only such summary continuous outcome measures, but not the actual numbers of responders, are reported in order to estimate response rates (probabilities) for different treatments and response ratios between treatments in such trials. The authors give some empirical justification, but encourage search for theoretical support and further empirical exploration. In particular, a problem that needs to be addressed is that randomness in baseline score is not taken into consideration. This will be done in the present paper. Assuming a binormal model for the data, we compare theoretically the true response rate for a single treatment arm to the theoretical response rate underlying two versions of the suggested imputation method. We also assess the performance of the method numerically for some choices of model parameters. We show that the method works satisfactorily in some cases, but can be seriously biased in others. Moreover, assessing the uncertainty of the estimates is problematic. We suggest an alternative Bayesian estimation procedure, based directly on the normal model, which avoids these problems and provides more precise estimates when applied to simulated data sets.     

Thermal stability of glucose and other sugar aldoses in normal phase high performance liquid chromatography

Analysis of glucose and other simple sugars are often performed by use of normal phase HPLC methods with acetonitrile as major eluent. The present results clearly show that column temperature plays an important role with respect to chromatographic performance and detection limits of glucose when using a specific carbohydrate column. A change in column temperature from 25 to 45 degrees C reduced the detection of glucose (with ELSD) by more than 41%, whereas the detection of other sugar aldoses (galactose, xylose and rhamnose) were suppressed even more. By increase of column temperature to 70 degrees C the detector signal of glucose was found to be less than 2% compared to that obtained at 20 degrees C. Neither fructose nor sucrose showed similar correlation between column temperature and detection. The rate of decreased response is not dependent on sample concentration or the ELSD settings. The results express the importance of accurate temperature control in the analysis of sugar aldoses, and also values low column temperatures for samples with low concentrations of sugar aldoses in order to improve detection.     

Synthesis of carbon nanofibers@MnO2 3D structures over copper foil as binder free anodes for lithiumion batteries

A 3D structured composite of carbon nanofibers@MnO2 on copper foil is reported here as a binder free anode of lithium ion batteries, with high capacity, fast charge/discharge rate and good stability. Carbon nanofiber yarns were synthesized directly over copper foil through a floating catalyst method. The growth of carbon nanofiber yarns was significantly enhanced by mechanical polishing of the copper foils, which can be attributed to the increased surface roughness and surface area of the copper foils. MnO2 was then grown over carbon nanofibers through spontaneous reduction of potassium permanganate by the carbon nanofibers. The obtained composites of carbon nanofibers@MnO2 over copper foil were tested as an anode in lithium ion batteries and they show superior electrochemical performance. The initial reversible capacity of carbon nanofibers@MnO2 reaches up to around 998 mAh g-1 at a rate of 60 mmA g-1 based on the mass of carbon nanofibers and MnO2 . The carbon nanofibers@MnO2 electrodes could deliver a capacity of 630 mAh g-1 at the beginning and maintain a capacity of 440 mmAh g-1 after 105 cycles at a rate of 600 mA g-1 . The high initial capacity can be attributed to the presence of porous carbon nanofiber yarns which have good electrical conductivity and the MnO2 thin film which makes the entire materials electrochemically active. The high cyclic stability of carbon nanofibers@MnO2 can be ascribed to the MnO2 thin film which can accommodate the volume expansion and shrinking during charge and discharge and the good contact of carbon nanofibers with MnO2 and copper foil.     

Synthesis of carbon nanofibers@MnO2 3D structures over copper foil as binder free anodes for lithium ion batteries

A 3D structured composite of carbon nanofibers@MnO₂ on copper foil is reported here as a binder free anode of lithium ion batteries, with high capacity, fast charge/discharge rate and good stability. Carbon nanofiber yarns were synthesized directly over copper foil through a floating catalyst method. The growth of carbon nanofiber yarns was significantly enhanced by mechanical polishing of the copper foils, which can be attributed to the increased surface roughness and surface area of the copper foils. MnO₂ was then grown over carbon nanofibers through spontaneous reduction of potassium permanganate by the carbon nanofibers. The obtained composites of carbon nanofibers@MnO₂ over copper foil were tested as an anode in lithium ion batteries and they show superior electrochemical performance. The initial reversible capacity of carbon nanofibers@MnO₂ reaches up to around 998 mAh·g^?1 at a rate of 60 mmA·g^?1 based on the mass of carbon nanofibers and MnO₂. The carbon nanofibers@MnO₂ electrodes could deliver a capacity of 630 mAh·g^?1 at the beginning and maintain a capacity of 440 mmAh·g^?1 after 105 cycles at a rate of 600 mA·g^?1. The high initial capacity can be attributed to the presence of porous carbon nanofiber yarns which have good electrical conductivity and the MnO₂ thin film which makes the entire materials electrochemically active. The high cyclic stability of carbon nanofibers@MnO₂ can be ascribed to the MnO₂ thin film which can accommodate the volume expansion and shrinking during charge and discharge and the good contact of carbon nanofibers with MnO₂ and copper foil.     

A Comparison of an Analytical Approach and a Standard Simulation Approach in Bayesian Forecasting Applied to Monthly Data from Insurance of Companies

In this paper, we apply the theory of Bayesian forecasting and dynamic linear models, as presented in West and Harrison (1997), to monthly data from insurance of companies. The total number reported claims of compensation is chosen to be the primary time series of interest. The model is decomposed into a trend block, a seasonal effects block and a regression block with a transformed number of policies as regressor. An essential part of the West and Harrison (1997) approach is to find optimal discount factors for each block and hence avoid the specification of the variance matrices of the error terms in the system equations. The BATS package of Pole et al. (1994) is applied in the analysis. We compare predictions based on this analytical approach with predictions based on a standard simulation approach applying the BUGS package of Spiegelhalter et al. (1995). The motivation for this comparison is to gain knowledge on the quality of predictions based on more or less standard simulation techniques in other applications where an analytical approach is impossible. The predicted values of the two approaches are very similar. The uncertainties, however, in the predictions based on the simulation approach are far larger especially two months or more ahead. This partly indicates the advantages of applying optimal discount factors and partly the disadvantages of at least a standard simulation approach for long term predictions.     

Discriminatory power, typability, and accuracy of single nucleotide extension microarrays

Despite great conceptual promise, the use of microarrays in typing approaches has not yet gained wide acceptance. The establishment of proper criteria for determining discriminatory power as well as typability and the accuracy of microarray data remains to be solved. Purely experimental estimations of these parameters would far exceed what is experimentally practical. We therefore used simulations in combination with experimental results in parameter estimations. Our assay was based on 26 single nucleotide polymorphisms (SNPs) identified in the Campylobacterjejuni Multi Locus Sequence Typing (MLST) database (http:///pubmist.org/campylobacter/). The SNPs were detected using a single nucleotide extension (SNE) typing microarray. Unknown isolates were assigned to the known sequence type(s) by calculating weighted sum of matches minus a weighted sum of mismatches between predicted and candidate genotype. The weights were set according to the Bayesian posterior probability of the SNP classification. These studies showed that any typing or profiling method based on binary data requires an accuracy of <2-3% error for each datapoint (in our case SNPs) to classify the isolates to the correct allelic profile in 90% of the cases. The classification error for our experimental data was 3.2% (after removing 5 high error SNPs). We therefore conclude that SNE microarrays are promising for future high-throughput typing of bacteria.