Static and Dynamic Mechanical Behavior of Electron Beam‐Cured Monomer and Monomer/Liquid Crystal Systems

Summary: This paper discusses static and dynamic mechanical properties of electron beam‐cured mixtures made of the nematic liquid crystal (LC) E7 and either tripropyleneglycol diacrylate (TPGDA) or propoxylated glycerol triacrylate (GPTA) as monomers differing essentially by their functionality. Dilution of the initial mixture with LC leads to a significant weakening of the film mechanical strength. Strong effects were found on Young modulus and rubbery state modulus. As the concentration of LC increases, these mechanical parameters drop significantly together with the glass transition temperature of the polymer showing a plasticizing effect. The results for the glass transition temperatures for the polymer and the LC were confirmed by thermograms obtained by differential scanning calorimetry.

Storage tensile modulus versus temperature of EB‐cured TPGDA/E7 films for different LC concentrations.     

A new spatiotemporal regularization approach for reconstruction of cardiac transmembrane potential patterns

The single-beat reconstruction of electrical cardiac sources from body-surface electrocardiogram data might become an important issue for clinical application. The feasibility and field of application of noninvasive imaging methods strongly depend on development of stable algorithms for solving the underlying ill-posed inverse problems. We propose a novel spatiotemporal regularization approach for the reconstruction of surface transmembrane potential (TMP) patterns. Regularization is achieved by imposing linearly formulated constraints on the solution in the spatial as well as in the temporal domain. In the spatial domain an operator similar to the surface Laplacian, weighted by a regularization parameter, is used. In the temporal domain monotonic nondecreasing behavior of the potential is presumed. This is formulated as side condition without the need of any regularization parameter. Compared to presuming template functions, the weaker temporal constraint widens the field of application because it enables the reconstruction of TMP patterns with ischemic and infarcted regions. Following the line of Tikhonov regularization, but considering all time points simultaneously, we obtain a linearly constrained sparse large-scale convex optimization problem solved by a fast interior point optimizer. We demonstrate the performance with simulations by comparing reconstructed TMP patterns with the underlying reference patterns.     

A comparison of noninvasive reconstruction of epicardial versus transmembrane potentials in consideration of the null space

We compare two source formulations for the electrocardiographic forward problem in consideration of their implications for regularizing the ill-posed inverse problem. The established epicardial potential source model is compared with a bidomain-theory-based transmembrane potential source formulation. The epicardial source approach is extended to the whole heart surface including the endocardial surfaces. We introduce the concept of the numerical null and signal space to draw attention to the problems associated with the nonuniqueness of the inverse solution and show that reconstruction of null-space components is an important issue for physiologically meaningful inverse solutions. Both formulations were tested with simulated data generated with an anisotropic heart model and with clinically measured data of two patients. A linear and a recently proposed quasi-linear inverse algorithm were applied for reconstructions of the epicardial and transmembrane potential, respectively. A direct comparison of both formulations was performed in terms of computed activation times. We found the transmembrane potential-based formulation is a more promising source formulation as stronger regularization by incorporation of biophysical a priori information is permitted.     

2,5-Dihydroxybenzoic acid butylamine and other ionic liquid matrixes for enhanced MALDI-MS analysis of biomolecules

The performance of the new ionic liquid MALDI-MS matrix 2,5-dihydroxybenzoic acid butylamine (DHBB) was assessed and compared to results obtained with the ionic liquid MALDI-MS matrixes alpha-cyano-4-hydroxycinnamic acid butylamine (CHCAB), 3,5-dimethoxycinnamic acid triethylamine (SinTri), and the frequently used solid MALDI matrixes 2,5-dihydroxybenzoic acid (DHB) and alpha-cyano-4-hydroxycinnamic acid (CHCA). The vacuum-stable, liquid consistency of ionic liquid matrix sample preparations considerably enhanced MALDI-MS analysis in terms of shot-to-shot reproducibility. Consequently, relative standard deviations serving as a measure for reproducibility of intensity-values acquired from 90 different spots on one MALDI-MS preparation were approximately one-half as high when solid DHB was replaced by the ionic liquid DHBB and eight times lower after exchange of solid CHCA by ionic liquid CHCAB. Interestingly, the ionic liquid MALDI matrix DHBB conserved the broad applicability of its solid analogue DHB, reduced MALDI induced fragmentation of monosialylated glycans and gangliosides, and was the superior ionic liquid matrix for MALDI-MS analysis of oligosaccharides and polymers, such as poly(ethylene glycol). It also worked well with glycoconjugates, peptides, and proteins; however, the tendency of DHBB to form multiple alkali adduct ions with peptides and proteins made CHCAB the ionic liquid matrix of choice for peptides. SinTri was the best ionic liquid matrix for proteins of high molecular weight, such as IgG. Furthermore, it was demonstrated for the first time that solvent properties and MALDI matrix properties of ionic liquids, such as DHBB, can be combined to enable fast, direct screening of an enzymatic reaction. This was proven by the desialylation of sialylactose with sialidase from Clostridium perfringens in the presence of diluted aqueous DHBB and subsequent direct MALDI-MS analysis of the reaction mixture.     

Open tubular immobilized metal ion affinity chromatography combined with MALDI MS and MS/MS for identification of protein phosphorylation sites

Protein phosphorylation is one of the most important known posttranslational modifications. Tandem mass spectrometry has become an important tool for mapping out the phosphorylation sites. However, when a peptide generated from the enzymatic or chemical digestion of a phosphoprotein is highly phosphorylated or contains many potential phosphorylation residues, phosphorylation site assignment becomes difficult. Separation and enrichment of phosphopeptides from a digest mixture is desirable and often a critical step for MS/MS-based site determination. In this work, we present a novel open tubular immobilized metal ion affinity chromatography (OT-IMAC) method, which is found to be more effective and reproducible for phosphopeptide enrichment, compared to a commonly used commercial product, Ziptip from Millipore. A strategy based on a combination of OT-IMAC, sequential dual-enzyme digestion, and matrix-assisted laser desorption/ionization (MALDI) quadrupole time-of-flight tandem mass spectrometry for phosphoprotein characterization is presented. It is shown that MALDI MS/MS with collision-induced dissociation can be very effective in generating fragment ion spectra containing rich structural information, which enables the identification of phosphorylation sites even from highly phosphorylated peptides. The applicability of this method for real world applications is demonstrated in the characterization and identification of phosphorylation sites of a Na(+)/H(+) exchanger fusion protein, His182, which was phosphorylated in vitro using the kinase Erk2.     

Isotropic sequence order learning

In this article, we present an isotropic unsupervised algorithm for temporal sequence learning. No special reward signal is used such that all inputs are completely isotropic. All input signals are bandpass filtered before converging onto a linear output neuron. All synaptic weights change according to the correlation of bandpass-filtered inputs with the derivative of the output. We investigate the algorithm in an open- and a closed-loop condition, the latter being defined by embedding the learning system into a behavioral feedback loop. In the open-loop condition, we find that the linear structure of the algorithm allows analytically calculating the shape of the weight change, which is strictly heterosynaptic and follows the shape of the weight change curves found in spike-time-dependent plasticity. Furthermore, we show that synaptic weights stabilize automatically when no more temporal differences exist between the inputs without additional normalizing measures. In the second part of this study, the algorithm is is placed in an environment that leads to closed sensor-motor loop. To this end, a robot is programmed with a prewired retraction reflex reaction in response to collisions. Through isotropic sequence order (ISO) learning, the robot achieves collision avoidance by learning the correlation between his early range-finder signals and the later occurring collision signal. Synaptic weights stabilize at the end of learning as theoretically predicted. Finally, we discuss the relation of ISO learning with other drive reinforcement models and with the commonly used temporal difference learning algorithm. This study is followed up by a mathematical analysis of the closed-loop situation in the companion article in this issue, "ISO Learning Approximates a Solution to the Inverse-Controller Problem in an Unsupervised Behavioral Paradigm" (pp. 865-884).     

ISO learning approximates a solution to the inverse-controller problem in an unsupervised behavioral paradigm

In "Isotropic Sequence Order Learning" (pp. 831-864 in this issue), we introduced a novel algorithm for temporal sequence learning (ISO learning). Here, we embed this algorithm into a formal nonevaluating (teacher free) environment, which establishes a sensor-motor feedback. The system is initially guided by a fixed reflex reaction, which has the objective disadvantage that it can react only after a disturbance has occurred. ISO learning eliminates this disadvantage by replacing the reflex-loop reactions with earlier anticipatory actions. In this article, we analytically demonstrate that this process can be understood in terms of control theory, showing that the system learns the inverse controller of its own reflex. Thereby, this system is able to learn a simple form of feedforward motor control.