A new sensitive method for the quantification of glyoxal and methylglyoxal in snow and ice by stir bar sorptive extraction and liquid desorption-HPLC-ESI-MS

In this study, the development of a new sensitive method for the analysis of alpha-dicarbonyls glyoxal (G) and methylglyoxal (MG) in environmental ice and snow is presented. Stir bar sorptive extraction with in situ derivatization and liquid desorption (SBSE-LD) was used for sample extraction, enrichment, and derivatization. Measurements were carried out using high-performance liquid chromatography coupled to electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS). As part of the method development, SBSE-LD parameters such as extraction time, derivatization reagent, desorption time and solvent, and the effect of NaCl addition on the SBSE efficiency as well as measurement parameters of HPLC-ESI-MS/MS were evaluated. Calibration was performed in the range of 1–60 ng/mL using spiked ultrapure water samples, thus incorporating the complete SBSE and derivatization process. 4-Fluorobenzaldehyde was applied as internal standard. Inter-batch precision was <12 % RSD. Recoveries were determined by means of spiked snow samples and were 78.9 ± 5.6 % for G and 82.7 ± 7.5 % for MG, respectively. Instrumental detection limits of 0.242 and 0.213 ng/mL for G and MG were achieved using the multiple reaction monitoring mode. Relative detection limits referred to a sample volume of 15 mL were 0.016 ng/mL for G and 0.014 ng/mL for MG. The optimized method was applied for the analysis of snow samples from Mount Hohenpeissenberg (close to the Meteorological Observatory Hohenpeissenberg, Germany) and samples from an ice core from Upper Grenzgletscher (Monte Rosa massif, Switzerland). Resulting concentrations were 0.085–16.3 ng/mL for G and 0.126–3.6 ng/mL for MG. Concentrations of G and MG in snow were 1–2 orders of magnitude higher than in ice core samples. The described method represents a simple, green, and sensitive analytical approach to measure G and MG in aqueous environmental samples.

Gas transport in firn: multiple-tracer characterisation and model intercomparison for NEEM, Northern Greenland

Air was sampled from the porous firn layer at the NEEM site in Northern Greenland. We use an ensemble of ten reference tracers of known atmospheric history to characterise the transport properties of the site. By analysing uncertainties in both data and the reference gas atmospheric histories, we can objectively assign weights to each of the gases used for the depth-diffusivity reconstruction. We define an objective root mean square criterion that is minimised in the model tuning procedure. Each tracer constrains the firn profile differently through its unique atmospheric history and free air diffusivity, making our multiple-tracer characterisation method a clear improvement over the commonly used single-tracer tuning. Six firn air transport models are tuned to the NEEM site; all models successfully reproduce the data within a 1σ Gaussian distribution. A comparison between two replicate boreholes drilled 64 m apart shows differences in measured mixing ratio profiles that exceed the experimental error. We find evidence that diffusivity does not vanish completely in the lock-in zone, as is commonly assumed. The ice age- gas age difference (1 age) at the firn-ice transition is calculated to be 182+3−9 yr. We further present the first intercomparison study of firn air models, where we introduce diagnostic scenarios designed to probe specific aspects of the model physics. Our results show that there are major differences in the way the models handle advective transport. Furthermore, diffusive fractionation of isotopes in the firn is poorly constrained by the models, which has consequences for attempts to reconstruct the isotopic composition of trace gases back in time using firn air and ice core records.

A novel treatment planning methodology for high dose 166Ho-DOTMP therapy in patients with multiple myeloma

Bone marrow ablation, i.e., the complete sterilization of the active bone marrow, followed by bone marrow transplantation (BMT) is a comment treatment of hematological malignancies. The use of targeted bone-seeking radiopharmaceuticals to selectively deliver radiation to the adjacent bone marrow cavities while sparing normal tissues is a promising technique. Current radiopharmaceutical treatment planning methods do not properly compensate for the patient-specific variable distribution of radioactive material within the skeleton. To improve the current method of internal dosimetry, novel methods for measuring the radiopharmaceutical distribution within the skeleton were developed. 99mTc-MDP was proven as an adequate surrogate for measuring 166Ho-DOTMP skeletal uptake and biodistribution, allowing these measures to be obtained faster, safer, and with higher spatial resolution. This translates directly into better measurements of the radiation dose distribution within the bone marrow. The resulting bone marrow dose-volume histograms allow prediction of the patient disease response where conventional organ scale dosimetry failed. They indicate that complete remission is only achieved when greater than 90% of the bone marrow receives at least 30 Gy. ^ Comprehensive treatment planning requires combining target and non-target organ dosimetry. Organs in the urinary tract were of special concern. The kidney dose is primarily dependent upon the mean transit time of 166 Ho-DOTMP through the kidney. Deconvolution analysis of renograms predicted a mean transit time of 2.6 minutes for 166Ho-DOTMP. The radiation dose to the urinary bladder wall is dependent upon numerous factors including patient hydration and void schedule. For beta-emitting isotopes such as 166Ho, reduction of the bladder wall dose is best accomplished through good patient hydration and ensuring a partially full bladder at the time of injection. Encouraging the patient to void frequently, or catheterizing the patient without irrigation, will not significantly reduce the bladder wall dose. ^ The results from this work will produce the most advanced treatment planning methodology for bone marrow ablation therapy using radioisotopes currently available. Treatments can be tailored specifically for each patient, including the addition of concomitant total body irradiation for patients with unfavorable dose distributions, to deliver a desired patient disease response, while minimizing the dose or toxicity to non-target organs. ^

Multiple gradient echo propeller (MGREP) MRI: Technical development and potential applications

The PROPELLER (Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction) magnetic resonance imaging (MRI) technique has inherent advantages over other fast imaging methods, including robust motion correction, reduced image distortion, and resistance to off-resonance effects. These features make PROPELLER highly desirable for T2*-sensitive imaging, high-resolution diffusion imaging, and many other applications. However, PROPELLER has been predominantly implemented as a fast spin-echo (FSE) technique, which is insensitive to T2* contrast, and requires time-inefficient signal averaging to achieve adequate signal-to-noise ratio (SNR) for many applications. These issues presently constrain the potential clinical utility of FSE-based PROPELLER. ^ In this research, our aim was to extend and enhance the potential applications of PROPELLER MRI by developing a novel multiple gradient echo PROPELLER (MGREP) technique that can overcome the aforementioned limitations. The MGREP pulse sequence was designed to acquire multiple gradient-echo images simultaneously, without any increase in total scan time or RF energy deposition relative to FSE-based PROPELLER. A new parameter was also introduced for direct user-control over gradient echo spacing, to allow variable sensitivity to T2* contrast. In parallel to pulse sequence development, an improved algorithm for motion correction was also developed and evaluated against the established method through extensive simulations. The potential advantages of MGREP over FSE-based PROPELLER were illustrated via three specific applications: (1) quantitative T2* measurement, (2) time-efficient signal averaging, and (3) high-resolution diffusion imaging. Relative to the FSE-PROPELLER method, the MGREP sequence was found to yield quantitative T2* values, increase SNR by ∼40% without any increase in acquisition time or RF energy deposition, and noticeably improve image quality in high-resolution diffusion maps. In addition, the new motion algorithm was found to improve the performance considerably in motion-artifact reduction. ^ Overall, this work demonstrated a number of enhancements and extensions to existing PROPELLER techniques. The new technical capabilities of PROPELLER imaging, developed in this thesis research, are expected to serve as the foundation for further expanding the scope of PROPELLER applications. ^

The information bottleneck method for genome-wide association studies

In population studies, most current methods focus on identifying one outcome-related SNP at a time by testing for differences of genotype frequencies between disease and healthy groups or among different population groups. However, testing a great number of SNPs simultaneously has a problem of multiple testing and will give false-positive results. Although, this problem can be effectively dealt with through several approaches such as Bonferroni correction, permutation testing and false discovery rates, patterns of the joint effects by several genes, each with weak effect, might not be able to be determined. With the availability of high-throughput genotyping technology, searching for multiple scattered SNPs over the whole genome and modeling their joint effect on the target variable has become possible. Exhaustive search of all SNP subsets is computationally infeasible for millions of SNPs in a genome-wide study. Several effective feature selection methods combined with classification functions have been proposed to search for an optimal SNP subset among big data sets where the number of feature SNPs far exceeds the number of observations. ^ In this study, we take two steps to achieve the goal. First we selected 1000 SNPs through an effective filter method and then we performed a feature selection wrapped around a classifier to identify an optimal SNP subset for predicting disease. And also we developed a novel classification method-sequential information bottleneck method wrapped inside different search algorithms to identify an optimal subset of SNPs for classifying the outcome variable. This new method was compared with the classical linear discriminant analysis in terms of classification performance. Finally, we performed chi-square test to look at the relationship between each SNP and disease from another point of view. ^ In general, our results show that filtering features using harmononic mean of sensitivity and specificity(HMSS) through linear discriminant analysis (LDA) is better than using LDA training accuracy or mutual information in our study. Our results also demonstrate that exhaustive search of a small subset with one SNP, two SNPs or 3 SNP subset based on best 100 composite 2-SNPs can find an optimal subset and further inclusion of more SNPs through heuristic algorithm doesn't always increase the performance of SNP subsets. Although sequential forward floating selection can be applied to prevent from the nesting effect of forward selection, it does not always out-perform the latter due to overfitting from observing more complex subset states. ^ Our results also indicate that HMSS as a criterion to evaluate the classification ability of a function can be used in imbalanced data without modifying the original dataset as against classification accuracy. Our four studies suggest that Sequential Information Bottleneck(sIB), a new unsupervised technique, can be adopted to predict the outcome and its ability to detect the target status is superior to the traditional LDA in the study. ^ From our results we can see that the best test probability-HMSS for predicting CVD, stroke,CAD and psoriasis through sIB is 0.59406, 0.641815, 0.645315 and 0.678658, respectively. In terms of group prediction accuracy, the highest test accuracy of sIB for diagnosing a normal status among controls can reach 0.708999, 0.863216, 0.639918 and 0.850275 respectively in the four studies if the test accuracy among cases is required to be not less than 0.4. On the other hand, the highest test accuracy of sIB for diagnosing a disease among cases can reach 0.748644, 0.789916, 0.705701 and 0.749436 respectively in the four studies if the test accuracy among controls is required to be at least 0.4. ^ A further genome-wide association study through Chi square test shows that there are no significant SNPs detected at the cut-off level 9.09451E-08 in the Framingham heart study of CVD. Study results in WTCCC can only detect two significant SNPs that are associated with CAD. In the genome-wide study of psoriasis most of top 20 SNP markers with impressive classification accuracy are also significantly associated with the disease through chi-square test at the cut-off value 1.11E-07. ^ Although our classification methods can achieve high accuracy in the study, complete descriptions of those classification results(95% confidence interval or statistical test of differences) require more cost-effective methods or efficient computing system, both of which can't be accomplished currently in our genome-wide study. We should also note that the purpose of this study is to identify subsets of SNPs with high prediction ability and those SNPs with good discriminant power are not necessary to be causal markers for the disease.^

A novel method for evaluating an interaction effect of correlated continuous covariates in a linear model

Interaction effect is an important scientific interest for many areas of research. Common approach for investigating the interaction effect of two continuous covariates on a response variable is through a cross-product term in multiple linear regression. In epidemiological studies, the two-way analysis of variance (ANOVA) type of method has also been utilized to examine the interaction effect by replacing the continuous covariates with their discretized levels. However, the implications of model assumptions of either approach have not been examined and the statistical validation has only focused on the general method, not specifically for the interaction effect.^ In this dissertation, we investigated the validity of both approaches based on the mathematical assumptions for non-skewed data. We showed that linear regression may not be an appropriate model when the interaction effect exists because it implies a highly skewed distribution for the response variable. We also showed that the normality and constant variance assumptions required by ANOVA are not satisfied in the model where the continuous covariates are replaced with their discretized levels. Therefore, naïve application of ANOVA method may lead to an incorrect conclusion. ^ Given the problems identified above, we proposed a novel method modifying from the traditional ANOVA approach to rigorously evaluate the interaction effect. The analytical expression of the interaction effect was derived based on the conditional distribution of the response variable given the discretized continuous covariates. A testing procedure that combines the p-values from each level of the discretized covariates was developed to test the overall significance of the interaction effect. According to the simulation study, the proposed method is more powerful then the least squares regression and the ANOVA method in detecting the interaction effect when data comes from a trivariate normal distribution. The proposed method was applied to a dataset from the National Institute of Neurological Disorders and Stroke (NINDS) tissue plasminogen activator (t-PA) stroke trial, and baseline age-by-weight interaction effect was found significant in predicting the change from baseline in NIHSS at Month-3 among patients received t-PA therapy.^

Separating ITCZ- and ENSO-related rainfall changes in the Galápagos over the last 3 kyr using D/H ratios of multiple lipid biomarkers

We present a 3000-yr rainfall reconstruction from the Galápagos Islands that is based on paired biomarker records from the sediment of El Junco Lake. Located in the eastern equatorial Pacific, the climate of the Galápagos Islands is governed by movements of the Intertropical Convergence Zone (ITCZ) and the El Niño-Southern Oscillation (ENSO). We use a novel method for reconstructing past ENSO- and ITCZ-related rainfall changes through analysis of molecular and isotopic biomarker records representing several types of plants and algae that grow under differing climatic conditions. We propose that ?D values of dinosterol, a sterol produced by dinoflagellates, record changes in mean rainfall in El Junco Lake, while dD values of C34 botryococcene, a hydrocarbon unique to the green alga Botryococcus braunii, record changes in rainfall associated with moderate-to-strong El Niño events. We use these proxies to infer changes in mean rainfall and El Niño-related rainfall over the past 3000 yr. During periods in which the inferred change in El Niño-related rainfall opposed the change in mean rainfall, we infer changes in the amount of ITCZ-related rainfall. Simulations with an idealized isotope hydrology model of El Junco Lake help illustrate the interpretation of these proxy reconstructions. Opposing changes in El Niño- and ITCZ-related rainfall appear to account for several of the largest inferred hydrologic changes in El Junco Lake. We propose that these reconstructions can be used to infer changes in frequency and/or intensity of El Niño events and changes in the position of the ITCZ in the eastern equatorial Pacific over the past 3000 yr. Comparison with El Junco Lake sediment grain size records indicates general agreement of inferred rainfall changes over the late Holocene.

Turbidity measurements in 96-wells microplates to determine the densities of copiotrophic and oligotrophic bacteria with the Most-Probable-Number-method during POLARSTERN cruise ANT-XI/2

A new microtiter-plate dilution method was applied during the expedition ANTARKTIS-XI/2 with RV Polarstern to determine the distribution of copiotrophic and oligotrophic bacteria in the water columns at polar fronts. Twofold serial dilutions were performed with an eight-channel Electrapette in 96-wells plates by mixing 150 µl of seawater with 150 µl of copiotrophic or olitrophic Trypticase-Broth, three times per well. After incubation of about 6 month at 2 °C, turbidities were measured with an eight-channel photometer at 405 nm and combinations of positive test results for three consecutive dilutions chosen and compared with a Most Probable Number table, calculated for 8 replicates and twofold serial dilutions. Densities of 12 to 661 cells/ml for copiotrophs, and 1 to 39 cells/ml for oligotrophs were found. Colony Forming Units on copiotrophic Trypticase-Agar were between 6 and 847 cells/ml, which is in the same range as determined with the MPN method.

Solid catadioptric telephoto lens design with SMS method

Two new optical structures are designed using the Simultaneous Multiple Surfaces (SMS) method, comprised of 2 reflecting surfaces and 2 refracting surfaces, 800mm focal length, f/8 (aperture diameter 100 mm) and 1.180 diagonal field of view in the SWIR band. The lens surfaces are rotational symmetric and calculated to have good control of non-paraxial rays. We have achieved designs with excellent performance, and with total system length of less than 60 mm.

Ultra-compact SWIR telephoto lens design with SMS method

In this work, we propose two new optical structures, using the Simultaneous Multiple Surfaces (SMS) method, comprised of 2 reflecting surfaces and 2 refracting surfaces, 800mm focal length, f/8 (aperture diameter 100 mm) and 1.18 0 diagonal field of view in the SWIR band. The lens surfaces are rotational symmetric and calculated to have good control of non-paraxial rays. We have achieved designs with excellent performance, and with total system length of less than 60 mm.

Overview of the SMS design method applied to imaging optics

The Simultaneous Multiple Surfaces (SMS) was developed as a design method in Nonimaging Optics during the 90s. Later, the method was extended for designing Imaging Optics. We present an overview of the method applied to imaging optics in planar (2D) geometry and compare the results with more classical designs based on achieving aplanatism of different orders. These classical designs are also viewed as particular cases of SMS designs. Systems with up to 4 aspheric surfaces are shown. The SMS design strategy is shown to perform always better than the classical design (in terms of image quality). Moreover, the SMS method is a direct method, i.e., it is not based in multi-parametric optimization techniques. This gives the SMS method an additional interest since it can be used for exploring solutions where the multiparameter techniques can get lost because of the multiple local minima

An application of the SMS method for imaging designs.

The Simultaneous Multiple Surface (SMS) method in planar geometry (2D) is applied to imaging designs, generating lenses that compare well with aplanatic designs. When the merit function utilizes image quality over the entire field (not just paraxial), the SMS strategy is superior. In fact, the traditional aplanatic approach is actually a particular case of the SMS strategy