Discriminating different classes of biological networks by analyzing the graphs spectra distribution

The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e.g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them on (1) protein-protein interaction networks of different species and (2) on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length) failed

Optimal feeding and swimming gaits of biflagellated organisms

Locomotion is widely observed in life at micrometric scales and is exhibited by many eukaryotic unicellular organisms. Motility of such organisms can be achieved through periodic deformations of a tail-like projection called the eukaryotic flagellum. Although the mechanism allowing the flagellum to deform is largely understood, questions related to the functional significance of the observed beating patterns remain unresolved. Here, we focus our attention on the stroke patterns of biflagellated phytoplanktons resembling the green alga Chlamydomonas. Such organisms have been widely observed to beat their flagella in two different ways - a breast-stroke and an undulatory stroke-both of which are prototypical of general beating patterns observed in eukaryotes. We develop a general optimization procedure to determine the existence of optimal swimming gaits and investigate their functional significance with respect to locomotion and nutrient uptake. Both the undulatory and the breaststroke represent local optima for efficient swimming. With respect to the generation of feeding currents, we found the breaststroke to be optimal and to enhance nutrient uptake significantly, particularly when the organism is immersed in a gradient of nutrients. Keywords: optimization; stroke kinematics; low Reynolds number; efficiencyNational Science Foundation (U.S.) (Grant CCF-0323672)National Science Foundation (U.S.) (Grant CTS-0624830

Deterministic reaction models with power-law forces

We study a one-dimensional particles system, in the overdamped limit, where nearest particles attract with a force inversely proportional to a power of their distance and coalesce upon encounter. The detailed shape of the distribution function for the gap between neighbouring particles serves to discriminate between different laws of attraction. We develop an exact Fokker-Planck approach for the infinite hierarchy of distribution functions for multiple adjacent gaps and solve it exactly, at the mean-field level, where correlations are ignored. The crucial role of correlations and their effect on the gap distribution function is explored both numerically and analytically. Finally, we analyse a random input of particles, which results in a stationary state where the effect of correlations is largely diminished

Analyzing staphylococcal contamination on surfaces and bedside areas of a neonatal intensive care unit of a children\u27s hospital

Staphylococci species are known to be a cause of Healthcare-Associated Infections (HAIs) in neonatal intensive care units (NICU). There is limited research about the surveillance and identification of staphylococci bacteria from NICUs. Surveillance of bacteria within the NICU helps to identify areas acting as reservoirs for bacteria so that new cleaning policies and techniques can be put in place to stop the spread of HAIs. The objective of this study was to swab sample sites in a local level IV hospital NICU and identify locations of staphylococci presence throughout the NICU. Forty-one swabs were selected from over 900 swabs collected from the NICU at Erlanger Hospital for testing at the University of Tennessee Chattanooga’s Clinical Infectious Disease Control lab. Using aseptic technique and standard microbiological procedures swab samples from the NICU were regrown as pure subcultures and tested using a variety of different tests, including the Remel RapID™ STAPH PLUS identification system to provide genus and species identities for the isolates. Of the 41 swabs selected, 17 different staphylococci species were observed, including Staphylococcus aureus and Staphylococcus epidermidis. The 17 different identified species were found on 45% of the swab sites throughout the NICU, with the most contaminated device being the suction yankauer (53%), and the highest contaminated surface being floors near sinks (41%). Further study for bacterial surveillance in the NICU will help to determine the best disinfection policies and cleaning practices for the decreased transmission of HAIs

Mir-34a Mimics Are Potential Therapeutic Agents for p53-Mutated and Chemo-Resistant Brain Tumour Cells

Chemotherapeutic drug resistance and relapse remains a major challenge for paediatric (medulloblastoma) and adult (glioblastoma) brain tumour treatment. Medulloblastoma tumours and cell lines with mutations in the p53 signalling pathway have been shown to be specifically insensitive to DNA damaging agents. The aim of this study was to investigate the potential of triggering cell death in p53 mutated medulloblastoma cells by a direct activation of pro-death signalling downstream of p53 activation. Since non-coding microRNAs (miRNAs) have the ability to fine tune the expression of a variety of target genes, orchestrating multiple downstream effects, we hypothesised that triggering the expression of a p53 target miRNA could induce cell death in chemo-resistant cells. Treatment with etoposide, increased miR-34a levels in a p53-dependent fashion and the level of miR-34a transcription was correlated with the cell sensitivity to etoposide. miR-34a activity was validated by measuring the expression levels of one of its well described target: the NADH dependent sirtuin1 (SIRT1). Whilst drugs directly targeting SIRT1, were potent to trigger cell death at high concentrations only, introduction of synthetic miR-34a mimics was able to induce cell death in p53 mutated medulloblastoma and glioblastoma cell lines. Our results show that the need of a functional p53 signaling pathway can be bypassed by direct activation of miR-34a in brain tumour cells