Huang DS et al. (2014) Curr Protein Pept Sci "Prediction of protein-protein interactions based on protein-protein correlation using least squares regression."

Han K, Deng S, Zhang L, Zhang H, Yang K, Huang DS

[Curr Protein Pept Sci, 2014]

In order to transform protein sequences into the feature vectors, several works have been done, such as computing auto covariance (AC), conjoint triad (CT), local descriptor (LD), moran autocorrelation (MA), normalized moreaubroto autocorrelation (NMB) and so on. In this paper, we shall adopt these transformation methods to encode the proteins, respectively, where AC, CT, LD, MA and NMB are all represented by '+' in a unified manner. A new method, i.e. the combination of least squares regression with '+' (abbreviated as LSR(+)), will be introduced for encoding a protein-protein correlation-based feature representation and an interacting protein pair. Thus there are totally five different combinations for LSR(+), i.e. LSRAC, LSRCT, LSRLD, LSRMA and LSRNMB. As a result, we combined a support vector machine (SVM) approach with LSR(+) to predict protein-protein interactions (PPI) and PPI networks. The proposed method has been applied on four datasets, i.e. Saaccharomyces cerevisiae, Escherichia coli, Homo sapiens and Caenorhabditis elegans. The experimental results demonstrate that all LSR(+) methods outperform many existing representative algorithms. Therefore, LSR(+) is a powerful tool to characterize the protein-protein correlations and to infer PPI, whilst keeping high performance on prediction of PPI networks.

Alcalde Cuesta F et al. (2016) Sci Rep "Exploring the topological sources of robustness against invasion in biological and technological networks."

Lozano Rojo A, Gonzalez Sequeiros P, Alcalde Cuesta F

[Sci Rep, 2016]

For a network, the accomplishment of its functions despite perturbations is called robustness. Although this property has been extensively studied, in most cases, the network is modified by removing nodes. In our approach, it is no longer perturbed by site percolation, but evolves after site invasion. The process transforming resident/healthy nodes into invader/mutant/diseased nodes is described by the Moran model. We explore the sources of robustness (or its counterpart, the propensity to spread favourable innovations) of the US high-voltage power grid network, the Internet2 academic network, and the C. elegans connectome. We compare them to three modular and non-modular benchmark networks, and samples of one thousand random networks with the same degree distribution. It is found that, contrary to what happens with networks of small order, fixation probability and robustness are poorly correlated with most of standard statistics, but they depend strongly on the degree distribution. While community detection techniques are able to detect the existence of a central core in Internet2, they are not effective in detecting hierarchical structures whose topological complexity arises from the repetition of a few rules. Box counting dimension and Rent's rule are applied to show a subtle trade-off between topological and wiring complexity.

Hernandez-Gonzalez A et al. (2016) Parasit Vectors "Evaluation of onchocerciasis seroprevalence in Bioko Island (Equatorial Guinea) after years of disease control programmes."

Aparicio P, Garate T, Nguema J, Moya L, Nguema R, Hernandez-Gonzalez A, Perteguer MJ, Benito A, Herrador Z

[Parasit Vectors, 2016]

BACKGROUND: Onchocerciasis or "river blindness" is a chronic parasitic disease caused by the filarial worm Onchocerca volvulus, transmitted through infected blackflies (Simulium spp.). Bioko Island (Equatorial Guinea) used to show a high endemicity for onchocerciasis. During the last years, the disease control programmes using different larvicides and ivermectin administration have considerably reduced the prevalence and intensity of infection. Based on this new epidemiological scenario, in the present work we aimed to assess the impact of the strategies applied against onchocerciasis in Bioko Island by an evaluation of IgG4 antibodies specific for recombinant Ov-16 in ELISA. METHODS: A cross-sectional study was conducted in Bioko Island from mid-January to mid-February, 2014. Twenty communities were randomly selected from rural and urban settings. A total of 140 households were chosen. In every selected household, all individuals aged 5years and above were recruited; 544 study participants agreed to be part of this work. No previous data on onchocerciasis seroprevalence in the selected communities were available. Blood samples were collected and used in an "ELISA in-house" prepared with recombinant Ov-16, expressed and further purified. IgG4 antibodies specific for recombinant Ov-16 were evaluated by ELISA in all of the participants. RESULTS: Based on the Ov-16 ELISA, the onchocerciasis seroprevalence was 7.9%, mainly concentrated in rural settings; samples from community Catedral Ela Nguema (# 16) were missed during the field work. Among the rural setups, communities Inasa Maule (# 7), Ruiche (# 20) and Barrios Adyacentes Riaba (# 14), had the highest seropositivity percentages (29.2, 26.9 and 23.8%, respectively). With respect to the urban settings, we did not find any positive case in communities Manzana Casa Bola (# 3), Colas Sesgas (# 6), Getesa (# 8), Moka Bioko (# 9), Impecsa (# 10), Baney Zona Baja (# 12) and Santo Tomas de Aquino (# 1). No onchocerciasis seropositive samples were found in 10-year-old individuals or younger. The IgG4 positive titles increased in older participants. CONCLUSIONS: A significant decline in onchocerciasis prevalence was observed in Bioko Island after years of disease-vector control and CDTI strategy. The seroprevalence increased with age, mainly in rural settings that could be due to previous exposure of population to the filarial parasite, eliminated by the control programmes introduced against onchocerciasis. A new Ov-16 serological evaluation with a larger sample size of children below 10years of age is required to demonstrate the interruption of transmission of O. volvulus in the human population of Bioko Island (Equatorial Guinea) according to the WHO criteria.