A La Autoantigen Homologue Is Required for the Internal Ribosome Entry Site Mediated Translation of Giardiavirus

Translation of Giardiavirus (GLV) mRNA is initiated at an internal ribosome entry site (IRES) in the viral transcript. The IRES localizes to a downstream portion of 5′ untranslated region (UTR) and a part of the early downstream coding region of the transcript. Recent studies indicated that the IRES does not require a pre-initiation complex to initiate translation but may directly recruit the small ribosome subunit with the help of a number of trans-activating protein factors. A La autoantigen homologue in the viral host Giardia lamblia, GlLa, was proposed as one of the potential trans-activating factors based on its specific binding to GLV-IRES in vitro. In this study, we further elucidated the functional role of GlLa in GLV-IRES mediated translation in Giardia by knocking down GlLa with antisense morpholino oligo, which resulted in a reduction of GLV-IRES activity by 40%. An over-expression of GlLa in Giardia moderately stimulated GLV-IRES activity by 20%. A yeast inhibitory RNA (IRNA), known to bind mammalian and yeast La autoantigen and inhibit Poliovirus and Hepatitis C virus IRES activities in vitro and in vivo, was also found to bind to GlLa protein in vitro and inhibited GLV-IRES function in vivo. The C-terminal domain of La autoantigen interferes with the dimerization of La and inhibits its function. An over-expression of the C-terminal domain (200–348aa) of GlLa in Giardia showed a dominant-negative effect on GLV-IRES activity, suggesting a potential inhibition of GlLa dimerization. HA tagged GlLa protein was detected mainly in the cytoplasm of Giardia, thus supporting a primary role of GlLa in translation initiation in Giardiavirus

Printing Technologies for Integration of Electronic Devices and Sensors

Many different methods, such as screen printing, gravure, flexography, inkjet etc., have been employed to print electronic devices. Depending on the type and performance of the devices, processing is done at low or high temperature using precursor- or particle-based inks. As a result of the processing details, devices can be fabricated on flexible or non-flexible substrates, depending on their temperature stability. Furthermore, in order to reduce the operating voltage, printed devices rely on high-capacitance electrolytes rather than on dielectrics. The printing resolution and speed are two of the major challenging parameters for printed electronics. High-resolution printing produces small-size printed devices and high-integration densities with minimum materials consumption. However, most printing methods have resolutions between 20 and 50 μm. Printing resolutions close to 1 μm have also been achieved with optimized process conditions and better printing technology. The final physical dimensions of the devices pose severe limitations on their performance. For example, the channel lengths being of this dimension affect the operating frequency of the thin-film transistors (TFTs), which is inversely proportional to the square of channel length. Consequently, short channels are favorable not only for high-frequency applications but also for high-density integration. The need to reduce this dimension to substantially smaller sizes than those possible with today’s printers can be fulfilled either by developing alternative printing or stamping techniques, or alternative transistor geometries. The development of a polymer pen lithography technique allows scaling up parallel printing of a large number of devices in one step, including the successive printing of different materials. The introduction of an alternative transistor geometry, namely the vertical Field Effect Transistor (vFET), is based on the idea to use the film thickness as the channel length, instead of the lateral dimensions of the printed structure, thus reducing the channel length by orders of magnitude. The improvements in printing technologies and the possibilities offered by nanotechnological approaches can result in unprecedented opportunities for the Internet of Things (IoT) and many other applications. The vision of printing functional materials, and not only colors as in conventional paper printing, is attractive to many researchers and industries because of the added opportunities when using flexible substrates such as polymers and textiles. Additionally, the reduction of costs opens new markets. The range of processing techniques covers laterally-structured and large-area printing technologies, thermal, laser and UV-annealing, as well as bonding techniques, etc. Materials, such as conducting, semiconducting, dielectric and sensing materials, rigid and flexible substrates, protective coating, organic, inorganic and polymeric substances, energy conversion and energy storage materials constitute an enormous challenge in their integration into complex devices

Fuzzy tissue classification for non-linear patient-specific biomechanical models for whole-body image registration

Comparison of whole-body medical images acquired for a given patient at different times is important for diagnosis, treatment assessment and surgery planning. Prior to comparison, the images need to be registered (aligned) as changes in the patient’s posture and other factors associated with skeletal motion and deformations of organs/tissues lead to differences between the images. For whole-body images, such differences are large, which poses challenges for traditionally used registration methods that rely solely on image processing techniques. Therefore, in our previous studies, we successfully applied image registration using patient-specific biomechanical models in which predicting deformations of organs/tissues is treated as a non-linear problem of computational mechanics. Constructing such models tends to be time-consuming as it involves tedious image segmentation which divides images into non-overlapping constituents with different material properties. To eliminate segmentation, we propose Fuzzy C-Means (FCM) classification to assign material properties at the integration points of a finite element mesh. In this study, we present an application of the FCM tissue classification algorithm and analyse sensitivity of the accuracy of whole-body image registration using non-linear patient-specific finite models to the FCM classification parameters. We show that accurate registration (within two times of the image voxel size) can be achieved