Front-End Electron Transfer Dissociation Coupled to a 21 Tesla FT-ICR Mass Spectrometer for Intact Protein Sequence Analysis

High resolution mass spectrometry is a key technology for in-depth protein characterization. High-field Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) enables high-level interrogation of intact proteins in the most detail to date. However, an appropriate complement of fragmentation technologies must be paired with FTMS to provide comprehensive sequence coverage, as well as characterization of sequence variants, and post-translational modifications. Here we describe the integration of front-end electron transfer dissociation (FETD) with a custom-built 21 tesla FT-ICR mass spectrometer, which yields unprecedented sequence coverage for proteins ranging from 2.8 to 29 kDa, without the need for extensive spectral averaging (e.g., ~60% sequence coverage for apo-myoglobin with four averaged acquisitions). The system is equipped with a multipole storage device separate from the ETD reaction device, which allows accumulation of multiple ETD fragment ion fills. Consequently, an optimally large product ion population is accumulated prior to transfer to the ICR cell for mass analysis, which improves mass spectral signal-to-noise ratio, dynamic range, and scan rate. We find a linear relationship between protein molecular weight and minimum number of ETD reaction fills to achieve optimum sequence coverage, thereby enabling more efficient use of instrument data acquisition time. Finally, real-time scaling of the number of ETD reactions fills during method-based acquisition is shown, and the implications for LC-MS/MS top-down analysis are discussed.

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Modeling mechanical properties of polyhydroxyalkanoate during degradation in animal tissue

Polyhydroxyalkanoates (PHAs) are a family of biodegradable and biocompatible polymers produced by several species microorganisms that possess favorable mechanical properties (e.g. strength and elongation properties). Different types of PHA polymers have been used in medical applications. However, in order to better understand the use of this polymer in the different applications, a thorough understanding of the kinetics of in vivo degradation is one of the major requirements. In this study, poly(3‐hydroxybutyrate) (PHB) was subcutaneously implanted in mice and incubated for 2, 4, 8, or 16 weeks. After removal from the animal, the strength, elongation, mass loss, and enthalpy of the PHB were tested for each time point. From these data, a mathematical model was generated by Rayleigh's method of dimensional analysis, where polymer strength over tissue contact time could be predicted. To prove the model, previous data obtained by our group were used: poly(3‐hydroxybutyrate‐co‐3‐hydroxyhexanoate) [P(HB‐co‐HHx)] incubation in the presence of human embryonic kidney cells (HEK). It was found that the developed model was aligned with experimental results, could predict the strength of the polymer when in contact with cells, and the predicted strength follows the trend of the experimental data. Also, the dimensionless constant (K) value associated with the model is different for both experiments, where this constant, produced via experimental data, is used for construction of a homogeneous equation. Copyright © 2017 John Wiley & Sons, Ltd.     

Effects of mechanical stretching on average orientation of cellulose and pectin in onion epidermis cell wall: A polarized FT-IR study

The organization of polysaccharides in plant cell walls is important for the mechanics of plant cells. Spectral analysis of cell walls by polarized IR can reveal polysaccharide organization, but may be complicated by dipoles not aligned with the backbone. For instance, analysis of uniaxially-aligned cellulose Iβ film revealed that the dipole transition vector of the 1160 cm^?1 band involving stretch vibrations of glycosidic C1–O–C4 linkages is approximately at 30° with respect to the backbone of the cellulose chain, because of coupling with C5–O–C1 bonds in the six-membered rings. In the case of homogalacturonan, the dipole transition vector of the ester carbonyl group vibration (ν_C=O, 1745 cm^?1) is expected to be nearly normal to the homogalacturonan backbone. Using this information and the dichroism equation, the change in net orientation of cell wall polymers upon mechanical stretch was determined by polarized IR analysis. Never-dried abaxial outer epidermal cell walls of the second scale of onion bulb were mechanically stretched along longitudinal or transverse directions with respect to the long axis of the cells and then dried while under mechanical stretch. The average orientations of both 1160 and 1745 cm^?1 vibration transition dipoles were rotated by ~5° and ~4°, respectively, along the stretch direction from their initial random distributions upon longitudinal strain by 14%; and by ~4° and ~3°, respectively, upon transverse strain by 12%. These results imply that both cellulose microfibrils and pectins in the cell wall are passively realigned along the stretch direction by external mechanical force. The analytical methodology developed here will be useful to study how cell wall polymers might reorganize during cell wall growth and development.     

A systematic study of the hydration and drying process of silica xerogels using Cu(II) EPR spectroscopy

Journal of Sol-Gel Science and Technology - The influence of the hydration and drying process on the line shape and signal intensity of the electron paramagnetic resonance spectra recorded from...     

A new coercivity condition applied to semipositone integral equations with nonpositive,unbounded nonlinearities and applications to nonlocal BVPs

We consider the perturbed Hammerstein integral equation

$$\begin{aligned} y(t)=\gamma _1(t)H_1(\varphi _1(y))+\gamma _2(t)H_2(\varphi _2(y))+\lambda \int _0^1G(t,s)f(s,y(s)) \mathrm{d}s \end{aligned}$$

and demonstrate the existence of at least one positive solution in the case where this problem is semipostione—i.e., f is allowed to be negative on its domain. As applications of this abstract result, we demonstrate existence of at least one positive solution to a variety of boundary value problems in the ordinary differential equations setting as well as the setting of elliptic partial differential equations on annuli. Finally, two novel aspects of this study are that, first, our results allow for f to be strictly negative on its entire domain, and second, it can actually be the case that \(\lim _{y\rightarrow +\infty }f(t,y)=-\infty \), uniformly for \(t\in [0,1]\). In addition, these results can hold even if \(H_1\) and \(H_2\) are piecewise linear on their domains.

     

Pattern‐avoiding permutations and Brownian excursion part I: Shapes and fluctuations

Permutations that avoid given patterns are among the most classical objects in combinatorics and have strong connections to many fields of mathematics, computer science and biology. In this paper we study the scaling limits of a random permutation avoiding a pattern of length 3 and their relations to Brownian excursion. Exploring this connection to Brownian excursion allows us to strengthen the recent results of Madras and Pehlivan [25] and Miner and Pak [29] as well as to understand many of the interesting phenomena that had previously gone unexplained. © 2016 Wiley Periodicals, Inc. Random Struct. Alg., 50, 394–419, 2017     

ThrEEBoost: Thresholded Boosting for Variable Selection and Prediction via Estimating Equations

Most variable selection techniques for high-dimensional models are designed to be used in settings, where observations are independent and completely observed. At the same time, there is a rich literature on approaches to estimation of low-dimensional parameters in the presence of correlation, missingness, measurement error, selection bias, and other characteristics of real data. In this article, we present ThrEEBoost (Thresholded EEBoost), a general-purpose variable selection technique which can accommodate such problem characteristics by replacing the gradient of the loss by an estimating function. ThrEEBoost generalizes the previously proposed EEBoost algorithm (Wolfson 2011 Wolfson, J. (2011), “EEBoost: A General Method for Prediction and Variable Selection Based on Estimating Equations,” Journal of the American Statistical Association, 106, 296–305.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) by allowing the number of regression coefficients updated at each step to be controlled by a thresholding parameter. Different thresholding parameter values yield different variable selection paths, greatly diversifying the set of models that can be explored; the optimal degree of thresholding can be chosen by cross-validation. ThrEEBoost was evaluated using simulation studies to assess the effects of different threshold values on prediction error, sensitivity, specificity, and the number of iterations to identify minimum prediction error under both sparse and nonsparse true models with correlated continuous outcomes. We show that when the true model is sparse, ThrEEBoost achieves similar prediction error to EEBoost while requiring fewer iterations to locate the set of coefficients yielding the minimum error. When the true model is less sparse, ThrEEBoost has lower prediction error than EEBoost and also finds the point yielding the minimum error more quickly. The technique is illustrated by applying it to the problem of identifying predictors of weight change in a longitudinal nutrition study. Supplementary materials are available online.     

Programming With Models: Writing Statistical Algorithms for General Model Structures With NIMBLE

We describe NIMBLE, a system for programming statistical algorithms for general model structures within R. NIMBLE is designed to meet three challenges: flexible model specification, a language for programming algorithms that can use different models, and a balance between high-level programmability and execution efficiency. For model specification, NIMBLE extends the BUGS language and creates model objects, which can manipulate variables, calculate log probability values, generate simulations, and query the relationships among variables. For algorithm programming, NIMBLE provides functions that operate with model objects using two stages of evaluation. The first stage allows specialization of a function to a particular model and/or nodes, such as creating a Metropolis-Hastings sampler for a particular block of nodes. The second stage allows repeated execution of computations using the results of the first stage. To achieve efficient second-stage computation, NIMBLE compiles models and functions via C++, using the Eigen library for linear algebra, and provides the user with an interface to compiled objects. The NIMBLE language represents a compilable domain-specific language (DSL) embedded within R. This article provides an overview of the design and rationale for NIMBLE along with illustrative examples including importance sampling, Markov chain Monte Carlo (MCMC) and Monte Carlo expectation maximization (MCEM). Supplementary materials for this article are available online.