Preservation of the biofunctionality of DNA and protein during microfabrication

Microfabrication processes, especially in silicon, are not compatible with biomolecules. Silicon and metal-based materials having crystalline structures are manipulated under harsh conditions with acids, bases, and organic solvents at high temperature. In comparison, organic biomolecules such as DNA and proteins have complex, three-dimensional structures and are sensitive to denaturation, oxidation, hydrolysis, and thermal destruction. Here, we report on the integration of DNA and the biotin-binding protein NeutrAvidin into microfabrication processes by using a novel approach based on a gold passivation mask. Our data show that this passivation method preserves approximately 84% of the biofunctionality of DNA and approximately 30% of that of NeutrAvidin under harsh process conditions. This novel technology enables the integration of DNA, proteins, and potentially other biological molecules into mass scalable microfabrication processes for biomedical devices, biochips, biosensors, and microelectromechanical systems with biomolecules (BioMEMS).     

PFG NMR and internal magnetic field gradients in plant-based materials

In this contribution, it is demonstrated that inner magnetic field gradients can seriously affect the results of stimulated echo PFG NMR experiments on plant-based materials even if there is no notable content of paramagnetic substances. Such effects could be observed both in experiments on water in pharmaceutical grade cellulose powder materials and on eggplant fruit tissue. In both cases, it was observed that the effects of internal magnetic field gradients led to different relative values of the diffusion coefficient compared to values obtained with a gradient-compensating pulse sequence.     

Molecular Structure of 3-Hydroxy-4-Pyrone

Abstract

Aerial parts of Erigeron annuus and E. strigosus afforded 3-hydroxy-4-pyrone, the molecular structure of which was determined by single crystal X-ray diffraction.     

BAMLSS: Bayesian Additive Models for Location,Scale, and Shape (and Beyond)

Bayesian analysis provides a convenient setting for the estimation of complex generalized additive regression models (GAMs). Since computational power has tremendously increased in the past decade, it is now possible to tackle complicated inferential problems, for example, with Markov chain Monte Carlo simulation, on virtually any modern computer. This is one of the reasons why Bayesian methods have become increasingly popular, leading to a number of highly specialized and optimized estimation engines and with attention shifting from conditional mean models to probabilistic distributional models capturing location, scale, shape (and other aspects) of the response distribution. To embed many different approaches suggested in literature and software, a unified modeling architecture for distributional GAMs is established that exploits distributions, estimation techniques (posterior mode or posterior mean), and model terms (fixed, random, smooth, spatial,…). It is shown that within this framework implementing algorithms for complex regression problems, as well as the integration of already existing software, is relatively straightforward. The usefulness is emphasized with two complex and computationally demanding application case studies: a large daily precipitation climatology, as well as a Cox model for continuous time with space-time interactions. Supplementary material for this article is available online.