A novel algorithm for digital infinite-impulse response (IIR) filter design is proposed in this paper. The suggested algorithm is a kind of cooperative coevolutionary genetic algorithm. It considers the magnitude response and the phase response simultaneously and also tries to find the lowest filter order. The structure and the coefficients of the digital IIR filter are coded separately, and they evolve coordinately as two different species, i.e., the control species and the coefficient species. The nondominated sorting genetic algorithm-II is used for the control species to guide the algorithms toward three objectives simultaneously. The simulated annealing is used for the coefficient species to keep the diversity. These two strategies make the cooperative coevolutionary process work effectively. Comparisons with another genetic algorithm-based digital IIR filter design method by numerical experiments show that the suggested algorithm is effective and robust in digital IIR filter design

The influenza M2 protein forms an acid-activated tetrameric proton channel important for the virus lifecycle. Residue His37 in the transmembrane domain is responsible for channel activation and proton selectivity. While the structure and dynamics of His37 have been well studied in TM peptide constructs, it has not been investigated in the presence of the full cytoplasmic domain, which increases the proton conductivity by 2-fold compared to the TM peptide. We report here ¹³C and ¹⁵N chemical shifts of His37 in the cytoplasmic-containing M2(21–97) and show that cationic histidines are already present at neutral pH, in contrast to the TM peptide, indicating that the cytoplasmic domain shifts the protonation equilibria. Quantification of the imidazole ¹⁵N intensities yielded two resolved proton dissociation constants (pK_a’s) of 7.1 and 5.4, which differ from the TM result but resemble the M2(18–60) result, suggesting cooperative proton binding. The average His37 pK_a is higher for M2(21–97) than for the shorter constructs. We attribute this higher pK_a to direct and indirect effects of the cytoplasmic domain, which is rich in acidic residues. 2D ¹³C–¹³C correlation spectra reveal seven His37 Cα-Cβ cross peaks at different pH, some of which are unique to the cytoplasmic-containing M2 and correspond to more ideal α-helical conformations. Based on the pH at which these chemical shifts appear and their side chain structures, we assign these conformations to His37 in differently charged tetramers. Thus, the cytoplasmic domain facilitates proton conduction through the transmembrane pore by modifying the His37-water proton exchange equilibria and the His37 backbone conformational distribution.

Convolutional neural network (CNN) has achieved great success in compressed sensing-based magnetic resonance imaging (CS-MRI). Latest deep networks for CS-MRI usually consist of a stack of sub-networks, each of which refines the former image prediction to a more accurate one. However, as sub-network number increases, the information in prior sub-networks has little influence on subsequent ones, which increases the training difficulties and limits the reconstruction performance of the deep model. In this paper, we propose a novel network named very deep densely connected network (VDDCN) for CS-MRI. Dense connections are introduced to connect any two sub-networks of VDDCN, so each sub-network can make full use of all former predictions, boosting the reconstruction performance of the whole network. The sub-network of VDDCN is composed of a feature extraction and fusion block (FEFB) processing data in image domain, and a data consistency (DC) layer enforcing the data fidelity in k-space. Specifically, in FEFB, multi-level features are extracted by recursive feature extraction and fusion sub-blocks (RFEFSBs) and fused locally to obtain compact features. VDDCN is much deeper than prior deep learning models, and able to discover more MR image details. Experimental results demonstrate that our proposed VDDCN outperforms other state-of-the-art CS-MRI methods.

As a perfect complement to conventional NMR that aims for chemical structure elucidation, Laplace NMR constitutes a powerful technique to study spin relaxation and diffusion, revealing information on molecular motions and spin interactions. Different from conventional NMR adopting Fourier transform to deal with the acquired data, Laplace NMR relies on specially designed signal processing and reconstruction algorithms resembling the inverse Laplace transform, and it generally faces severe challenges in cases where high spectral resolution and high spectral dimensionality are required. Herein, based on the tensor technique for high-dimensional problems and the sparsity assumption, we propose a general method for high-resolution reconstruction of multidimensional Laplace NMR data. We show that the proposed method can reconstruct multidimensional Laplace NMR spectra in a high-resolution manner for exponentially decaying relaxation and diffusion data acquired by commercial NMR instruments. Therefore, it would broaden the scope of multidimensional Laplace NMR applications.