Does Training Laparoscopic Skills in a Virtual Reality Simulator Improve Surgical Performance?

Background and Purpose: Several different methods of teaching laparoscopic skills have been advocated, with virtual reality surgical simulation (VRSS) being the most popular. Its effectiveness in improving surgical performance is not a consensus yet, however. The purpose of this study was to determine whether practicing surgical skills in a virtual reality simulator results in improved surgical performance. Materials and Methods: Fifteen medical students recruited for the study were divided into three groups. Group I (control) did not receive any VRSS training. For 10 weeks, group II trained basic laparoscopic skills (camera handling, cutting skill, peg transfer skill, and clipping skill) in a VRSS laparoscopic skills simulator. Group III practiced the same skills and, in addition, performed a simulated cholecystectomy. All students then performed a cholecystectomy in a swine model. Their performance was reviewed by two experienced surgeons. The following parameters were evaluated: Gallbladder pedicle dissection time, clipping time, time for cutting the pedicle, gallbladder removal time, total procedure time, and blood loss. Results: With practice, there was improvement in most of the evaluated parameters by each of the individuals. There were no statistical differences in any of evaluated parameters between those who did and did not undergo VRSS training, however. Conclusion: VRSS training is assumed to be an effective tool for learning and practicing laparoscopic skills. In this study, we could not demonstrate that VRSS training resulted in improved surgical performance. It may be useful, however, in familiarizing surgeons with laparoscopic surgery. More effective methods of teaching laparoscopic skills should be evaluated to help in improving surgical performance.

The Brain as a Distributed Intelligent Processing System: An EEG Study

Background: Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS), first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS. Methodology and Principal Findings: In our study, a new EEG brain mapping technique, based on the neural efficiency hypothesis and the notion of the brain as a Distributed Intelligence Processing System, was used to investigate the correlations between IQ evaluated with WAIS (Whechsler Adult Intelligence Scale) and WISC (Wechsler Intelligence Scale for Children), and the brain activity associated with visual and verbal processing, in order to test the validity of a distributed neural basis for intelligence. Conclusion: The present results support these claims and the neural efficiency hypothesis.

Unsuccessful attacks dominate a drone-preying wasp's hunting performance near stingless bee nests

Bee males (drones) of stingless bees tend to congregate near entrances of conspecific nests, where they wait for virgin queens that initiate their nuptial flight. We observed that the Neotropical solitary wasp Trachypus boharti (Hymenoptera, Cabronidae) specifically preys on males of the stingless bee Scaptotrigona postica (Hymenoptera, Apidae); these wasps captured up to 50 males per day near the entrance of a single hive. Over 90% of the wasp attacks were unsuccessful; such erroneous attacks often involved conspecific wasps and worker bees. After the capture of non-male prey, wasps almost immediately released these individuals unharmed and continued hunting. A simple behavioral experiment showed that at short distances wasps were not specifically attracted to S. postica males nor were they repelled by workers of the same species. Likely, short-range prey detection near the bees' nest is achieved mainly by vision whereas close-range prey recognition is based principally on chemical and/or mechanical cues. We argue that the dependence on the wasp's visual perception during attack and the crowded and dynamic hunting conditions caused wasps to make many preying attempts that failed. Two wasp-density-related factors, wasp-prey distance and wasp-wasp encounters, may account for the fact that the highest male capture and unsuccessful wasp bee encounter rates occurred at intermediate wasp numbers.

PARTITIONED ANALYSIS FOR DIMENSIONALLY-HETEROGENEOUS HYDRAULIC NETWORKS

In this work an iterative strategy is developed to tackle the problem of coupling dimensionally-heterogeneous models in the context of fluid mechanics. The procedure proposed here makes use of a reinterpretation of the original problem as a nonlinear interface problem for which classical nonlinear solvers can be applied. Strong coupling of the partitions is achieved while dealing with different codes for each partition, each code in black-box mode. The main application for which this procedure is envisaged arises when modeling hydraulic networks in which complex and simple subsystems are treated using detailed and simplified models, correspondingly. The potentialities and the performance of the strategy are assessed through several examples involving transient flows and complex network configurations.

Generalized connectivity between any two nodes in a complex network

This article focuses on the identification of the number of paths with different lengths between pairs of nodes in complex networks and how these paths can be used for characterization of topological properties of theoretical and real-world complex networks. This analysis revealed that the number of paths can provide a better discrimination of network models than traditional network measurements. In addition, the analysis of real-world networks suggests that the long-range connectivity tends to be limited in these networks and may be strongly related to network growth and organization.

High performance magnetic separation of gold nanoparticles for catalytic oxidation of alcohols

We present the magnetic separation approach to facilitate the recovery of gold nanoparticle (AuNP) catalysts. The use of magnetically recoverable supports for the immobilization of AuNPs instead of traditional oxides, polymers or carbon based solids guarantees facile, clean, fast and efficient separation of the catalyst at the end of the reaction cycle. Magnetic separation can be considered an environmentally benign separation approach, since it minimizes the use of auxiliary substances and energy for achieving catalyst recovery. The catalyst preparation is based on the immobilization of Au(3+) on the surface of core-shell silica-coated magnetite nanoparticles, followed by metal reduction using two different methods. AuNPs were prepared by thermal reduction in air and by hydrogen reduction at mild temperature. Interestingly, the mean particle size of the supported AuNPs was similar (ca. 5.9 nm), but the polydispersity of the samples is quite different. The catalytic activity of both catalysts in the aerobic oxidation of alcohols was investigated and a distinct selectivity for benzyl alcohol oxidation was observed.

Finding Quasi-Optimal Network Topologies for Information Transmission in Active Networks

This work clarifies the relation between network circuit (topology) and behaviour (information transmission and synchronization) in active networks, e.g. neural networks. As an application, we show how one can find network topologies that are able to transmit a large amount of information, possess a large number of communication channels, and are robust under large variations of the network coupling configuration. This theoretical approach is general and does not depend on the particular dynamic of the elements forming the network, since the network topology can be determined by finding a Laplacian matrix (the matrix that describes the connections and the coupling strengths among the elements) whose eigenvalues satisfy some special conditions. To illustrate our ideas and theoretical approaches, we use neural networks of electrically connected chaotic Hindmarsh-Rose neurons.

Network of recurrent events for the Olami-Feder-Christensen model

We numerically study the dynamics of a discrete spring-block model introduced by Olami, Feder, and Christensen (OFC) to mimic earthquakes and investigate to what extent this simple model is able to reproduce the observed spatiotemporal clustering of seismicity. Following a recently proposed method to characterize such clustering by networks of recurrent events [J. Davidsen, P. Grassberger, and M. Paczuski, Geophys. Res. Lett. 33, L11304 (2006)], we find that for synthetic catalogs generated by the OFC model these networks have many nontrivial statistical properties. This includes characteristic degree distributions, very similar to what has been observed for real seismicity. There are, however, also significant differences between the OFC model and earthquake catalogs, indicating that this simple model is insufficient to account for certain aspects of the spatiotemporal clustering of seismicity.

Automatic Network Fingerprinting through Single-Node Motifs

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs-a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.

Knowledge acquisition by networks of interacting agents in the presence of observation errors

In this work we investigate knowledge acquisition as performed by multiple agents interacting as they infer, under the presence of observation errors, respective models of a complex system. We focus the specific case in which, at each time step, each agent takes into account its current observation as well as the average of the models of its neighbors. The agents are connected by a network of interaction of Erdos-Renyi or Barabasi-Albert type. First, we investigate situations in which one of the agents has a different probability of observation error (higher or lower). It is shown that the influence of this special agent over the quality of the models inferred by the rest of the network can be substantial, varying linearly with the respective degree of the agent with different estimation error. In case the degree of this agent is taken as a respective fitness parameter, the effect of the different estimation error is even more pronounced, becoming superlinear. To complement our analysis, we provide the analytical solution of the overall performance of the system. We also investigate the knowledge acquisition dynamic when the agents are grouped into communities. We verify that the inclusion of edges between agents (within a community) having higher probability of observation error promotes the loss of quality in the estimation of the agents in the other communities.

Social interaction as a heuristic for combinatorial optimization problems

We investigate the performance of a variant of Axelrod's model for dissemination of culture-the Adaptive Culture Heuristic (ACH)-on solving an NP-Complete optimization problem, namely, the classification of binary input patterns of size F by a Boolean Binary Perceptron. In this heuristic, N agents, characterized by binary strings of length F which represent possible solutions to the optimization problem, are fixed at the sites of a square lattice and interact with their nearest neighbors only. The interactions are such that the agents' strings (or cultures) become more similar to the low-cost strings of their neighbors resulting in the dissemination of these strings across the lattice. Eventually the dynamics freezes into a homogeneous absorbing configuration in which all agents exhibit identical solutions to the optimization problem. We find through extensive simulations that the probability of finding the optimal solution is a function of the reduced variable F/N(1/4) so that the number of agents must increase with the fourth power of the problem size, N proportional to F(4), to guarantee a fixed probability of success. In this case, we find that the relaxation time to reach an absorbing configuration scales with F(6) which can be interpreted as the overall computational cost of the ACH to find an optimal set of weights for a Boolean binary perceptron, given a fixed probability of success.

Haplotype distribution of five nuclear genes based on network genealogies and Bayesian inference indicates that Trypanosoma cruzi hybrid strains are polyphyletic

Chagas disease is still a major public health problem in Latin America. Its causative agent, Trypanosoma cruzi, can be typed into three major groups, T. cruzi I, T. cruzi II and hybrids. These groups each have specific genetic characteristics and epidemiological distributions. Several highly virulent strains are found in the hybrid group; their origin is still a matter of debate. The null hypothesis is that the hybrids are of polyphyletic origin, evolving independently from various hybridization events. The alternative hypothesis is that all extant hybrid strains originated from a single hybridization event. We sequenced both alleles of genes encoding EF-1 alpha, actin and SSU rDNA of 26 T. cruzi strains and DHFR-TS and TR of 12 strains. This information was used for network genealogy analysis and Bayesian phylogenies. We found T. cruzi I and T. cruzi II to be monophyletic and that all hybrids had different combinations of T. cruzi I and T. cruzi II haplotypes plus hybrid-specific haplotypes. Bootstrap values (networks) and posterior probabilities (Bayesian phylogenies) of clades supporting the monophyly of hybrids were far below the 95% confidence interval, indicating that the hybrid group is polyphyletic. We hypothesize that T. cruzi I and T. cruzi II are two different species and that the hybrids are extant representatives of independent events of genome hybridization, which sporadically have sufficient fitness to impact on the epidemiology of Chagas disease.

High performance gold nanorods and silver nanocubes in surface-enhanced Raman spectroscopy of pesticides

The behavior of Au nanorods and Ag nanocubes as analytical sensors was evaluated for three different classes of herbicides. The use of such anisotropic nanoparticles in surface-enhanced Raman scattering (SERS) experiments allows the one to obtain the spectrum of crystal violet dye in the single molecule regime, as well as the pesticides dichlorophenoxyacetic acid (2,4-D), trichlorfon and ametryn. Such metallic substrates show high SERS performance at low analyte concentrations making them adequate for use as analytical sensors. Density functional theory (DFT) calculations of the geometries and vibrational wavenumbers of the adsorbates in the presence of silver or gold atoms were used to elucidate the nature of adsorbate-nanostructure bonding in each case and support the enhancement patterns observed in each SERS spectrum.

Validation of dot blot hybridization and denaturing high performance liquid chromatography as reliable methods for TP53 codon 72 genotyping in molecular epidemiologic studies

Background: Mutations in TP53 are common events during carcinogenesis. In addition to gene mutations, several reports have focused on TP53 polymorphisms as risk factors for malignant disease. Many studies have highlighted that the status of the TP53 codon 72 polymorphism could influence cancer susceptibility. However, the results have been inconsistent and various methodological features can contribute to departures from Hardy-Weinberg equilibrium, a condition that may influence the disease risk estimates. The most widely accepted method of detecting genotyping error is to confirm genotypes by sequencing and/or via a separate method. Results: We developed two new genotyping methods for TP53 codon 72 polymorphism detection: Denaturing High Performance Liquid Chromatography (DHPLC) and Dot Blot hybridization. These methods were compared with Restriction Fragment Length Polymorphism (RFLP) using two different restriction enzymes. We observed high agreement among all methodologies assayed. Dot-blot hybridization and DHPLC results were more highly concordant with each other than when either of these methods was compared with RFLP. Conclusions: Although variations may occur, our results indicate that DHPLC and Dot Blot hybridization can be used as reliable screening methods for TP53 codon 72 polymorphism detection, especially in molecular epidemiologic studies, where high throughput methodologies are required.