Synthesis of stable peptide nucleic acid-modified gold nanoparticles and their assembly onto gold surfaces

PNA does it better: Peptide nucleic acid (PNA) oligomers were attached to gold nanoparticles (AuNPs) through a variety of mono- and trithiol linkers. These functionalized particles had sufficient stability for sequence-specific self-assembly onto gold surfaces (see figure) in the absence of ions or surfactants. The nanoparticle surface densities obtained were superior to comparable DNA-modified AuNPs

Improvement of downstream processing of recombinant proteins by means of genetic engineering

Flaschel E, Friehs K. Improvement of downstream processing of recombinant proteins by means of genetic engineering. Biotechnology Advances. 1993;11(1):31-77.The rapid advancement of genetic engineering has allowed to produce an impressive number of proteins on a scale which would not have been achieved by classical biotechnology. At the beginning of this development research was focussed on elucidating the mechanisms of protein overexpression. The appearance of inclusion bodies may illustrate the success. In the meantime, genetic engineering is not only expected to achieve overexpression, but to improve the whole process of protein production. For downstream processing of recombinant proteins, the synthesis of fusion proteins is of primary importance. Fusion with certain proteins or peptides may protect the target protein from proteolytic degradation and may alter its solubility. Intracellular proteins may be translocated by means of fusions with signal peptides. Affinity tags as fusion complements may render protein separation and purification highly selective. These methods as well as similar ones for improving the downstream processing of proteins will be discussed on the basis of recent literature

Applications of RNA-interference and Virus Induced Gene Silencing (VIGS) for Nutritional Genomics in Crop Plants.

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A review of unsupervised feature learning and deep learning for time-series modeling

This paper gives a review of the recent developments in deep learning and unsupervised feature learning for time-series problems. While these techniques have shown promise for modeling static data, such as computer vision, applying them to time-series data is gaining increasing attention. This paper overviews the particular challenges present in time-series data and provides a review of the works that have either applied time-series data to unsupervised feature learning algorithms or alternatively have contributed to modifications of feature learning algorithms to take into account the challenges present in time-series data