A tool for the qualitative comparison of membrane-embedded and detergent-solubilized membrane protein structures in projection

The calculation of projection structures (PSs) from Protein Data Bank (PDB)-coordinate files of membrane proteins is not well-established. Reports on such attempts exist but are rare. In addition, the different procedures are barely described and thus difficult if not impossible to reproduce. Here we present a simple, fast and well-documented method for the calculation and visualization of PSs from PDB-coordinate files of membrane proteins: the projection structure visualization (PSV)-method. The PSV-method was successfully validated using the PS of aquaporin-1 (AQP1) from 2D crystals and cryo-transmission electron microscopy, and the PDB-coordinate file of AQP1 determined from 3D crystals and X-ray crystallography. Besides AQP1, which is a relatively rigid protein, we also studied a flexible membrane transport protein, i.e. the L-arginine/agmatine antiporter AdiC. Comparison of PSs calculated from the existing PDB-coordinate files of substrate-free and L-arginine-bound AdiC indicated that conformational changes are detected in projection. Importantly, structural differences were found between the PSV-method calculated PSs of the detergent-solubilized AdiC proteins and the PS from cryo-TEM of membrane-embedded AdiC. These differences are particularly exciting since they may reflect a different conformation of AdiC induced by the lateral pressure in the lipid bilayer.

Experimental Comparison of Bluetooth and WiFi Signal Propagation for Indoor Localisation

Systems for indoor positioning using radio technologies are largely studied due to their convenience and the market opportunities they offer. The positioning algorithms typically derive geographic coordinates from observed radio signals and hence good understanding of the indoor radio channel is required. In this paper we investigate several factors that affect signal propagation indoors for both Bluetooth and WiFi. Our goal is to investigate which factors can be disregarded and which should be considered in the development of a positioning algorithm. Our results show that technical factors such as device characteristics have smaller impact on the signal than multipath propagation. Moreover, we show that propagation conditions differ in each direction. We also noticed that WiFi and Bluetooth, despite operating in the same radio band, do not at all times exhibit the same behaviour.

PCR-based diagnosis of Naegleria sp. infection in formalin-fixed and paraffin-embedded brain sections

We developed a real-time PCR which allowed the highly sensitive detection of Naegleria fowleri in histological brain tissue sections from experimentally infected mice. This genus-specific small-subunit (18S) rRNA gene-based PCR can complement conventional (immuno-) histology for the diagnosis of primary amoebic meningoencephalitis in paraffin-embedded brain necropsy specimens that had been fixed in formalin buffered with phosphate-buffered saline.

Gene expression variation between distinct areas of breast cancer measured from paraffin-embedded tissue cores

BACKGROUND: Diagnosis and prognosis in breast cancer are mainly based on histology and immunohistochemistry of formalin-fixed, paraffin-embedded (FFPE) material. Recently, gene expression analysis was shown to elucidate the biological variance between tumors and molecular markers were identified that led to new classification systems that provided better prognostic and predictive parameters. Archived FFPE samples represent an ideal source of tissue for translational research, as millions of tissue blocks exist from routine diagnostics and from clinical studies. These should be exploited to provide clinicians with more accurate prognostic and predictive information. Unfortunately, RNA derived from FFPE material is partially degraded and chemically modified and reliable gene expression measurement has only become successful after implementing novel and optimized procedures for RNA isolation, demodification and detection. METHODS: In this study we used tissue cylinders as known from the construction of tissue microarrays. RNA was isolated with a robust protocol recently developed for RNA derived from FFPE material. Gene expression was measured by quantitative reverse transcription PCR. RESULTS: Sixteen tissue blocks from 7 patients diagnosed with multiple histological subtypes of breast cancer were available for this study. After verification of appropriate localization, sufficient RNA yield and quality, 30 tissue cores were available for gene expression measurement on TaqMan(R) Low Density Arrays (16 invasive ductal carcinoma (IDC), 8 ductal carcinoma in situ (DCIS) and 6 normal tissue), and 14 tissue cores were lost. Gene expression values were used to calculate scores representing the proliferation status (PRO), the estrogen receptor status and the HER2 status. The PRO scores measured from entire sections were similar to PRO scores determined from IDC tissue cores. Scores determined from normal tissue cores consistently revealed lower PRO scores than cores derived from IDC or DCIS of the same block or from different blocks of the same patient. CONCLUSION: We have developed optimized protocols for RNA isolation from histologically distinct areas. RNA prepared from FFPE tissue cores is suitable for gene expression measurement by quantitative PCR. Distinct molecular scores could be determined from different cores of the same tumor specimen.

Learning flexible sensori-motor mappings in a complex network

Given the complex structure of the brain, how can synaptic plasticity explain the learning and forgetting of associations when these are continuously changing? We address this question by studying different reinforcement learning rules in a multilayer network in order to reproduce monkey behavior in a visuomotor association task. Our model can only reproduce the learning performance of the monkey if the synaptic modifications depend on the pre- and postsynaptic activity, and if the intrinsic level of stochasticity is low. This favored learning rule is based on reward modulated Hebbian synaptic plasticity and shows the interesting feature that the learning performance does not substantially degrade when adding layers to the network, even for a complex problem.