An FPGA Implementation to Detect Selective Cationic Antibacterial Peptides

Exhaustive prediction of physicochemical properties of peptide sequences is used in different areas of biological research. One example is the identification of selective cationic antibacterial peptides (SCAPs), which may be used in the treatment of different diseases. Due to the discrete nature of peptide sequences, the physicochemical properties calculation is considered a high-performance computing problem. A competitive solution for this class of problems is to embed algorithms into dedicated hardware. In the present work we present the adaptation, design and implementation of an algorithm for SCAPs prediction into a Field Programmable Gate Array (FPGA) platform. Four physicochemical properties codes useful in the identification of peptide sequences with potential selective antibacterial activity were implemented into an FPGA board. The speed-up gained in a single-copy implementation was up to 108 times compared with a single Intel processor cycle for cycle. The inherent scalability of our design allows for replication of this code into multiple FPGA cards and consequently improvements in speed are possible. Our results show the first embedded SCAPs prediction solution described and constitutes the grounds to efficiently perform the exhaustive analysis of the sequence-physicochemical properties relationship of peptides

Privatization in the presence of foreign competition and strategic policies

Recent evidence shows that developing and transition economies are increasingly privatizing their public firms and also experiencing rapid growth of inward foreign direct investment (FDI). In an international mixed oligopoly with strategic tax/subsidy policies, we analyze the interaction between privatization and FDI. We find that the incentive for FDI increases with privatization. However, the possibility of FDI reduces the degree of privatization. Our paper shows that FDI policies reducing the fixed-cost of undertaking FDI may need to complement the privatization policies to attract FDI and to improve domestic welfare

Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers

Accurate prediction of clinical changes of mild cognitive impairment (MCI) patients, including both qualitative change (i.e., conversion to Alzheimer's disease (AD)) and quantitative change (i.e., cognitive scores) at future time points, is important for early diagnosis of AD and for monitoring the disease progression. In this paper, we propose to predict future clinical changes of MCI patients by using both baseline and longitudinal multimodality data. To do this, we first develop a longitudinal feature selection method to jointly select brain regions across multiple time points for each modality. Specifically, for each time point, we train a sparse linear regression model by using the imaging data and the corresponding clinical scores, with an extra ‘group regularization’ to group the weights corresponding to the same brain region across multiple time points together and to allow for selection of brain regions based on the strength of multiple time points jointly. Then, to further reflect the longitudinal changes on the selected brain regions, we extract a set of longitudinal features from the original baseline and longitudinal data. Finally, we combine all features on the selected brain regions, from different modalities, for prediction by using our previously proposed multi-kernel SVM. We validate our method on 88 ADNI MCI subjects, with both MRI and FDG-PET data and the corresponding clinical scores (i.e., MMSE and ADAS-Cog) at 5 different time points. We first predict the clinical scores (MMSE and ADAS-Cog) at 24-month by using the multimodality data at previous time points, and then predict the conversion of MCI to AD by using the multimodality data at time points which are at least 6-month ahead of the conversion. The results on both sets of experiments show that our proposed method can achieve better performance in predicting future clinical changes of MCI patients than the conventional methods

cisRED: a database system for genome-scale computational discovery of regulatory elements

We describe cisRED, a database for conserved regulatory elements that are identified and ranked by a genome-scale computational system (). The database and high-throughput predictive pipeline are designed to address diverse target genomes in the context of rapidly evolving data resources and tools. Motifs are predicted in promoter regions using multiple discovery methods applied to sequence sets that include corresponding sequence regions from vertebrates. We estimate motif significance by applying discovery and post-processing methods to randomized sequence sets that are adaptively derived from target sequence sets, retain motifs with p-values below a threshold and identify groups of similar motifs and co-occurring motif patterns. The database offers information on atomic motifs, motif groups and patterns. It is web-accessible, and can be queried directly, downloaded or installed locally

Structural remodeling and oligomerization of human cathelicidin on membranes suggest fibril-like structures as active species

Antimicrobial peptides as part of the mammalian innate immune system target and remove major bacterial pathogens, often through irreversible damage of their cellular membranes. To explore the mechanism by which the important cathelicidin peptide LL-37 of the human innate immune system interacts with membranes, we performed biochemical, biophysical and structural studies. The crystal structure of LL-37 displays dimers of anti-parallel helices and the formation of amphipathic surfaces. Peptide-detergent interactions introduce remodeling of this structure after occupation of defined hydrophobic sites at the dimer interface. Furthermore, hydrophobic nests are shaped between dimer structures providing another scaffold enclosing detergents. Both scaffolds underline the potential of LL-37 to form defined peptide-lipid complexes in vivo. After adopting the activated peptide conformation LL-37 can polymerize and selectively extract bacterial lipids whereby the membrane is destabilized. The supramolecular fibril-like architectures formed in crystals can be reproduced in a peptide-lipid system after nanogold-labelled LL-37 interacted with lipid vesicles as followed by electron microscopy. We suggest that these supramolecular structures represent the LL-37-membrane active state. Collectively, our study provides new insights into the fascinating plasticity of LL-37 demonstrated at atomic resolution and opens the venue for LL-37-based molecules as novel antibiotics.We would like to thank Sandra Delgado for the technical help in the preparation of the cryoEM vitrified grids and Dr. Isabel Uson and Dr. Ivan De Marino for the Arcimboldo software and valuable help. Funding was provided by the Unidad de Biofisica and the IKERBASQUE and MINECO science foundations

A Fast and Reliable Method for Simultaneous Waveform, Amplitude and Latency Estimation of Single-Trial EEG/MEG Data

The amplitude and latency of single-trial EEG/MEG signals may provide valuable information concerning human brain functioning. In this article we propose a new method to reliably estimate single-trial amplitude and latency of EEG/MEG signals. The advantages of the method are fourfold. First, no a-priori specified template function is required. Second, the method allows for multiple signals that may vary independently in amplitude and/or latency. Third, the method is less sensitive to noise as it models data with a parsimonious set of basis functions. Finally, the method is very fast since it is based on an iterative linear least squares algorithm. A simulation study shows that the method yields reliable estimates under different levels of latency variation and signal-to-noise ratioÕs. Furthermore, it shows that the existence of multiple signals can be correctly determined. An application to empirical data from a choice reaction time study indicates that the method describes these data accurately