Iodine

by Joshua M. Epstein, Ramesh Pankajakshan, Ross A. Hammond

We introduce a novel hybrid of two fields—Computational Fluid Dynamics (CFD) and Agent-Based Modeling (ABM)—as a powerful new technique for urban evacuation planning. CFD is a predominant technique for modeling airborne transport of contaminants, while ABM is a powerful approach for modeling social dynamics in populations of adaptive individuals. The hybrid CFD-ABM method is capable of simulating how large, spatially-distributed populations might respond to a physically realistic contaminant plume. We demonstrate the overall feasibility of CFD-ABM evacuation design, using the case of a hypothetical aerosol release in Los Angeles to explore potential effectiveness of various policy regimes. We conclude by arguing that this new approach can be powerfully applied to arbitrary population centers, offering an unprecedented preparedness and catastrophic event response tool.

by Nicolas Christou, Ivo D. Dinov

Many scientific investigations depend on obtaining data-driven, accurate, robust and computationally-tractable parameter estimates. In the face of unavoidable intrinsic variability, there are different algorithmic approaches, prior assumptions and fundamental principles for computing point and interval estimates. Efficient and reliable parameter estimation is critical in making inference about observable experiments, summarizing process characteristics and prediction of experimental behaviors. In this manuscript, we demonstrate simulation, construction, validation and interpretation of confidence intervals, under various assumptions, using the interactive web-based tools provided by the Statistics Online Computational Resource (http://www.SOCR.ucla.edu). Specifically, we present confidence interval examples for population means, with known or unknown population standard deviation; population variance; population proportion (exact and approximate), as well as confidence intervals based on bootstrapping or the asymptotic properties of the maximum likelihood estimates. Like all SOCR resources, these confidence interval resources may be openly accessed via an Internet-connected Java-enabled browser. The SOCR confidence interval applet enables the user to empirically explore and investigate the effects of the confidence-level, the sample-size and parameter of interest on the corresponding confidence interval. Two applications of the new interval estimation computational library are presented. The first one is a simulation of confidence interval estimating the US unemployment rate and the second application demonstrates the computations of point and interval estimates of hippocampal surface complexity for Alzheimers disease patients, mild cognitive impairment subjects and asymptomatic controls.

by Naohiro Kurotaki, Shinya Tasaki, Hiroyuki Mishima, Shinji Ono, Akira Imamura, Taeko Kikuchi, Nao Nishida, Katsushi Tokunaga, Koh-ichiro Yoshiura, Hiroki Ozawa

The recent development of high-resolution DNA microarrays, in which hundreds of thousands of single nucleotide polymorphisms (SNPs) are genotyped, enables the rapid identification of susceptibility genes for complex diseases. Clusters of these SNPs may show runs of homozygosity (ROHs) that can be analyzed for association with disease. An analysis of patients whose parents were first cousins enables the search for autozygous segments in their offspring. Here, using the Affymetrix® Genome-Wide Human SNP Array 5.0 to determine ROHs, we genotyped 9 individuals with schizophrenia (SCZ) whose parents were first cousins. We identified overlapping ROHs on chromosomes 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 19, 20, and 21 in at least 3 individuals. Only the locus on chromosome 5 has been reported previously. The ROHs on chromosome 5q23.3–q31.1 include the candidate genes histidine triad nucleotide binding protein 1 (HINT1) and acyl-CoA synthetase long-chain family member 6 (ACSL6). Other overlapping ROHs may contain novel rare recessive variants that affect SCZ specifically in our samples, given the highly heterozygous nature of SCZ. Analysis of patients whose parents are first cousins may provide new insights for the genetic analysis of psychiatric diseases.

by Vladislav Volman, Matjaž Perc, Maxim Bazhenov

Electrical synapses (gap junctions) play a pivotal role in the synchronization of neuronal ensembles which also makes them likely agonists of pathological brain activity. Although large body of experimental data and theoretical considerations indicate that coupling neurons by electrical synapses promotes synchronous activity (and thus is potentially epileptogenic), some recent evidence questions the hypothesis of gap junctions being among purely epileptogenic factors. In particular, an expression of inter-neuronal gap junctions is often found to be higher after the experimentally induced seizures than before. Here we used a computational modeling approach to address the role of neuronal gap junctions in shaping the stability of a network to perturbations that are often associated with the onset of epileptic seizures. We show that under some circumstances, the addition of gap junctions can increase the dynamical stability of a network and thus suppress the collective electrical activity associated with seizures. This implies that the experimentally observed post-seizure additions of gap junctions could serve to prevent further escalations, suggesting furthermore that they are a consequence of an adaptive response of the neuronal network to the pathological activity. However, if the seizures are strong and persistent, our model predicts the existence of a critical tipping point after which additional gap junctions no longer suppress but strongly facilitate the escalation of epileptic seizures. Our results thus reveal a complex role of electrical coupling in relation to epileptiform events. Which dynamic scenario (seizure suppression or seizure escalation) is ultimately adopted by the network depends critically on the strength and duration of seizures, in turn emphasizing the importance of temporal and causal aspects when linking gap junctions with epilepsy.

by Tiina A. Riihimäki, Sampo Kukkurainen, Suvi Varjonen, Jarno Hörhä, Thomas K. M. Nyholm, Markku S. Kulomaa, Vesa P. Hytönen

Background

Avidin is a chicken egg-white protein with high affinity to vitamin H, also known as D-biotin. Many applications in life science research are based on this strong interaction. Avidin is a homotetrameric protein, which promotes its modification to symmetrical entities. Dual-chain avidin, a genetically engineered avidin form, has two circularly permuted chicken avidin monomers that are tandem-fused into one polypeptide chain. This form of avidin enables independent modification of the two domains, including the two biotin-binding pockets; however, decreased yields in protein production, compared to wt avidin, and complicated genetic manipulation of two highly similar DNA sequences in the tandem gene have limited the use of dual-chain avidin in biotechnological applications.

Principal Findings

To overcome challenges associated with the original dual-chain avidin, we developed chimeric dual-chain avidin, which is a tandem fusion of avidin and avidin-related protein 4 (AVR4), another member of the chicken avidin gene family. We observed an increase in protein production and better thermal stability, compared with the original dual-chain avidin. Additionally, PCR amplification of the hybrid gene was more efficient, thus enabling more convenient and straightforward modification of the dual-chain avidin. When studied closer, the generated chimeric dual-chain avidin showed biphasic biotin dissociation.

Significance

The improved dual-chain avidin introduced here increases its potential for future applications. This molecule offers a valuable base for developing bi-functional avidin tools for bioseparation, carrier proteins, and nanoscale adapters. Additionally, this strategy could be helpful when generating hetero-oligomers from other oligomeric proteins with high structural similarity.

by Meng Qi, Pan Wang, Nicholas O'Toole, Perry S. Barboza, Emilio Ungerfeld, Mary Beth Leigh, L. Brent Selinger, Greg Butler, Adrian Tsang, Tim A. McAllister, Robert J. Forster

Background

Herbivores rely on digestive tract lignocellulolytic microorganisms, including bacteria, fungi and protozoa, to derive energy and carbon from plant cell wall polysaccharides. Culture independent metagenomic studies have been used to reveal the genetic content of the bacterial species within gut microbiomes. However, the nature of the genes encoded by eukaryotic protozoa and fungi within these environments has not been explored using metagenomic or metatranscriptomic approaches.

Methodology/Principal Findings

In this study, a metatranscriptomic approach was used to investigate the functional diversity of the eukaryotic microorganisms within the rumen of muskoxen (Ovibos moschatus), with a focus on plant cell wall degrading enzymes. Polyadenylated RNA (mRNA) was sequenced on the Illumina Genome Analyzer II system and 2.8 gigabases of sequences were obtained and 59129 contigs assembled. Plant cell wall degrading enzyme modules including glycoside hydrolases, carbohydrate esterases and polysaccharide lyases were identified from over 2500 contigs. These included a number of glycoside hydrolase family 6 (GH6), GH48 and swollenin modules, which have rarely been described in previous gut metagenomic studies.

Conclusions/Significance

The muskoxen rumen metatranscriptome demonstrates a much higher percentage of cellulase enzyme discovery and an 8.7x higher rate of total carbohydrate active enzyme discovery per gigabase of sequence than previous rumen metagenomes. This study provides a snapshot of eukaryotic gene expression in the muskoxen rumen, and identifies a number of candidate genes coding for potentially valuable lignocellulolytic enzymes.

by Ying Liang, Peng Hang Chiu, Kit Yan Yip, Siu Yuen Chan

SUN2 is an inner nuclear membrane protein with a conserved Sad1/UNC-84 homology SUN-domain at the C-terminus. Intriguingly, SUN2 has also been reported to interact with Rab5, which localizes in early endosomes. To clarify the dual subcellular localization of SUN2, we investigated its localization in lamin A/C deficient cells rescued with lamin A or lamin C isoform, and in HeLa cells transfected with Rab5 or its mutants. We found that expression of lamin A but not lamin C partly restored the nuclear envelope localization of SUN2. SUN2 was redistributed to endosomes upon overexpression of Rab5, but remained on the nuclear envelope when the SUN domain was deleted. To explore the physiological function of SUN2 in vesicle trafficking and endocytosis, we demonstrated the colocalization of endogenous SUN2 and Rab5. Moreover, overexpression of SUN2 stimulated the uptake of transferrin while suppression of SUN2 expression attenuated the process. These findings support a role of SUN2 in endocytosis.

by José M. Galán, Maciej M. Łatek, Seyed M. Mussavi Rizi

Metanorms is a mechanism proposed to promote cooperation in social dilemmas. Recent experimental results show that network structures that underlie social interactions influence the emergence of norms that promote cooperation. We generalize Axelrod's analysis of metanorms dynamics to interactions unfolding on networks through simulation and mathematical modeling. Network topology strongly influences the effectiveness of the metanorms mechanism in establishing cooperation. In particular, we find that average degree, clustering coefficient and the average number of triplets per node play key roles in sustaining or collapsing cooperation.

by Chin-Sheng Yu, Chih-Hao Lu

For the first time, multiple sets of n-peptide compositions from antifreeze protein (AFP) sequences of various cold-adapted fish and insects were analyzed using support vector machine and genetic algorithms. The identification of AFPs is difficult because they exist as evolutionarily divergent types, and because their sequences and structures are present in limited numbers in currently available databases. Our results reveal that it is feasible to identify the shared sequential features among the various structural types of AFPs. Moreover, we were able to identify residues involved in ice binding without requiring knowledge of the three-dimensional structures of these AFPs. This approach should be useful for genomic and proteomic studies involving cold-adapted organisms.

by Yijun Wang, Tzyy-Ping Jung

Electroencephalogram (EEG) based brain-computer interfaces (BCI) have been studied since the 1970s. Currently, the main focus of BCI research lies on the clinical use, which aims to provide a new communication channel to patients with motor disabilities to improve their quality of life. However, the BCI technology can also be used to improve human performance for normal healthy users. Although this application has been proposed for a long time, little progress has been made in real-world practices due to technical limits of EEG. To overcome the bottleneck of low single-user BCI performance, this study proposes a collaborative paradigm to improve overall BCI performance by integrating information from multiple users. To test the feasibility of a collaborative BCI, this study quantitatively compares the classification accuracies of collaborative and single-user BCI applied to the EEG data collected from 20 subjects in a movement-planning experiment. This study also explores three different methods for fusing and analyzing EEG data from multiple subjects: (1) Event-related potentials (ERP) averaging, (2) Feature concatenating, and (3) Voting. In a demonstration system using the Voting method, the classification accuracy of predicting movement directions (reaching left vs. reaching right) was enhanced substantially from 66% to 80%, 88%, 93%, and 95% as the numbers of subjects increased from 1 to 5, 10, 15, and 20, respectively. Furthermore, the decision of reaching direction could be made around 100–250 ms earlier than the subject's actual motor response by decoding the ERP activities arising mainly from the posterior parietal cortex (PPC), which are related to the processing of visuomotor transmission. Taken together, these results suggest that a collaborative BCI can effectively fuse brain activities of a group of people to improve the overall performance of natural human behavior.