The C-terminus of PufX plays a key role in dimerisation and assembly of the reaction center light-harvesting 1 complex from Rhodobacter sphaeroides.

In bacterial photosynthesis reaction center-light-harvesting 1 (RC-LH1) complexes trap absorbed solar energy by generating a charge separated state. Subsequent electron and proton transfers form a quinol, destined to diffuse to the cytochrome bc1 complex. In bacteria such as Rhodobacter (Rba.) sphaeroides and Rba. capsulatus the PufX polypeptide creates a channel for quinone/quinol traffic across the LH1 complex that surrounds the RC, and it is therefore essential for photosynthetic growth. PufX also plays a key role in dimerization of the RC-LH1-PufX core complex, and the structure of the Rba. sphaeroides complex shows that the PufX C-terminus, particularly the region from ×49-×53, likely mediates association of core monomers. To investigate this putative interaction we analysed mutations PufX R49L, PufX R53L, PufX R49/53L and PufX G52L by measuring photosynthetic growth, fractionation of detergent-solubilised membranes, formation of 2-D crystals and electron microscopy. We show that these mutations do not affect assembly of PufX within the core or photosynthetic growth but they do prevent dimerization, consistent with predictions from the RC-LH1-PufX structure. We obtained low resolution structures of monomeric core complexes with and without PufX, using electron microscopy of negatively stained single particles and 3D reconstruction; the monomeric complex with PufX corresponds to one half of the dimer structure whereas LH1 completely encloses the RC if the gene encoding PufX is deleted. On the basis of the insights gained from these mutagenesis and structural analyses we propose a sequence for assembly of the dimeric RC-LH1-PufX complex

B-spline neural networks based PID controller for Hammerstein systems

A new PID tuning and controller approach is introduced for Hammerstein systems based on input/output data. A B-spline neural network is used to model the nonlinear static function in the Hammerstein system. The control signal is composed of a PID controller together with a correction term. In order to update the control signal, the multi-step ahead predictions of the Hammerstein system based on the B-spline neural networks and the associated Jacobians matrix are calculated using the De Boor algorithms including both the functional and derivative recursions. A numerical example is utilized to demonstrate the efficacy of the proposed approaches

Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes.

Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes

B-spline neural networks based PID controller for Hammerstein systems

A new PID tuning and controller approach is introduced for Hammerstein systems based on input/output data. A B-spline neural network is used to model the nonlinear static function in the Hammerstein system. The control signal is composed of a PID controller together with a correction term. In order to update the control signal, the multi-step ahead predictions of the Hammerstein system based on the B-spline neural networks and the associated Jacobians matrix are calculated using the De Boor algorithms including both the functional and derivative recursions. A numerical example is utilized to demonstrate the efficacy of the proposed approaches