Diffusion mechanisms of localised knots along a polymer

We consider the diffusive motion of a localized knot along a linear polymer chain. In particular, we derive the mean diffusion time of the knot before it escapes from the chain once it gets close to one of the chain ends. Self-reptation of the entire chain between either end and the knot position, during which the knot is provided with free volume, leads to an L^3 scaling of diffusion time; for sufficiently long chains, subdiffusion will enhance this time even more. Conversely, we propose local ``breathing'', i.e., local conformational rearrangement inside the knot region (KR) and its immediate neighbourhood, as additional mechanism. The contribution of KR-breathing to the diffusion time scales only quadratically, L^2, speeding up the knot escape considerably and guaranteeing finite knot mobility even for very long chains.Comment: 7 pages, 2 figures. Accepted to Europhys. Let

Avaliação da formação de compostos fenólicos em resíduos contendo benzeno após aplicação de processo fenton.

Projeto/Plano de Ação: 16.300.30004-00

CFD Models of a Serpentine Inlet, Fan, and Nozzle

Several computational fluid dynamics (CFD) codes were used to analyze the Versatile Integrated Inlet Propulsion Aerodynamics Rig (VIIPAR) located at NASA Glenn Research Center. The rig consists of a serpentine inlet, a rake assembly, inlet guide vanes, a 12-in. diameter tip-turbine driven fan stage, exit rakes or probes, and an exhaust nozzle with a translating centerbody. The analyses were done to develop computational capabilities for modeling inlet/fan interaction and to help interpret experimental data. Three-dimensional Reynolds averaged Navier-Stokes (RANS) calculations of the fan stage were used to predict the operating line of the stage, the effects of leakage from the turbine stream, and the effects of inlet guide vane (IGV) setting angle. Coupled axisymmetric calculations of a bellmouth, fan, and nozzle were used to develop techniques for coupling codes together and to investigate possible effects of the nozzle on the fan. RANS calculations of the serpentine inlet were coupled to Euler calculations of the fan to investigate the complete inlet/fan system. Computed wall static pressures along the inlet centerline agreed reasonably well with experimental data but computed total pressures at the aerodynamic interface plane (AIP) showed significant differences from the data. Inlet distortion was shown to reduce the fan corrected flow and pressure ratio, and was not completely eliminated by passage through the fa

The evolution of representation in simple cognitive networks

Representations are internal models of the environment that can provide guidance to a behaving agent, even in the absence of sensory information. It is not clear how representations are developed and whether or not they are necessary or even essential for intelligent behavior. We argue here that the ability to represent relevant features of the environment is the expected consequence of an adaptive process, give a formal definition of representation based on information theory, and quantify it with a measure R. To measure how R changes over time, we evolve two types of networks---an artificial neural network and a network of hidden Markov gates---to solve a categorization task using a genetic algorithm. We find that the capacity to represent increases during evolutionary adaptation, and that agents form representations of their environment during their lifetime. This ability allows the agents to act on sensorial inputs in the context of their acquired representations and enables complex and context-dependent behavior. We examine which concepts (features of the environment) our networks are representing, how the representations are logically encoded in the networks, and how they form as an agent behaves to solve a task. We conclude that R should be able to quantify the representations within any cognitive system, and should be predictive of an agent's long-term adaptive success.Comment: 36 pages, 10 figures, one Tabl

Comparing cystatin C and creatinine in the diagnosis of pediatric acute renal allograft dysfunction

Allograft function following renal transplantation is commonly monitored using serum creatinine. Multiple cross-sectional studies have shown that serum cystatin C is superior to creatinine for detection of mild to moderate chronic kidney dysfunction. Recent data in adults indicate that cystatin C might also be a more sensitive marker of acute renal dysfunction. This study aims to compare cystatin C and creatinine for detection of acute allograft dysfunction in children using pediatric RIFLE (risk of renal dysfunction, injury to the kidney, failure or loss of kidney function, end stage renal disease) criteria for acute kidney injury. Retrospective chart review of post-transplant period in 24 patients in whom creatinine and cystatin C were measured every day. Allograft dysfunction was defined as a sustained rise in marker concentration above the mean of the three preceding measurements. In total, there were 13 episodes of allograft dysfunction. Maximum RIFLE stages with creatinine were 'R' in 7, 'I' in 4, and 'F' in 2, with cystatin C 'R' in 6, 'I' in 4 and 'F' in 3, respectively. In 9/13 cases, both markers rose simultaneously, in three, the rise in creatinine preceded cystatin C by 1-5 days (median 4). In one case, the rise in cystatin C preceded creatinine by 1 day. The time lag was not statistically different. The maximum relative rise of creatinine was significantly higher than cystatin C. By multiple linear regression analysis, the maximum rise of cystatin C was related to the maximum rise of creatinine, but independent of patient age, gender, steroid dose, and anthropometric data. In this pediatric population, cystatin C was not superior to creatinine for the detection of acute allograft dysfunctio

miR-122 Stimulates Hepatitis C Virus RNA Synthesis by Altering the Balance of Viral RNAs Engaged in Replication versus Translation

SummaryThe liver-specific microRNA, miR-122, stabilizes hepatitis C virus (HCV) RNA genomes by recruiting host argonaute 2 (AGO2) to the 5′ end and preventing decay mediated by exonuclease Xrn1. However, HCV replication requires miR-122 in Xrn1-depleted cells, indicating additional functions. We show that miR-122 enhances HCV RNA levels by altering the fraction of HCV genomes available for RNA synthesis. Exogenous miR-122 increases viral RNA and protein levels in Xrn1-depleted cells, with enhanced RNA synthesis occurring before heightened protein synthesis. Inhibiting protein translation with puromycin blocks miR-122-mediated increases in RNA synthesis, but independently enhances RNA synthesis by releasing ribosomes from viral genomes. Additionally, miR-122 reduces the fraction of viral genomes engaged in protein translation. Depleting AGO2 or PCBP2, which binds HCV RNA in competition with miR-122 and promotes translation, eliminates miR-122 stimulation of RNA synthesis. Thus, by displacing PCBP2, miR-122 reduces HCV genomes engaged in translation while increasing the fraction available for RNA synthesis

Integrated photonics enables continuous-beam electron phase modulation

Integrated photonics facilitates extensive control over fundamental light–matter interactions in manifold quantum systems including atoms1, trapped ions2,3, quantum dots4 and defect centres5. Ultrafast electron microscopy has recently made free-electron beams the subject of laser-based quantum manipulation and characterization6,7,8,9,10,11, enabling the observation of free-electron quantum walks12,13,14, attosecond electron pulses10,15,16,17 and holographic electromagnetic imaging18. Chip-based photonics19,20 promises unique applications in nanoscale quantum control and sensing but remains to be realized in electron microscopy. Here we merge integrated photonics with electron microscopy, demonstrating coherent phase modulation of a continuous electron beam using a silicon nitride microresonator. The high-finesse (Q0 ≈ 106) cavity enhancement and a waveguide designed for phase matching lead to efficient electron–light scattering at extremely low, continuous-wave optical powers. Specifically, we fully deplete the initial electron state at a cavity-coupled power of only 5.35 microwatts and generate >500 electron energy sidebands for several milliwatts. Moreover, we probe unidirectional intracavity fields with microelectronvolt resolution in electron-energy-gain spectroscopy21. The fibre-coupled photonic structures feature single-optical-mode electron–light interaction with full control over the input and output light. This approach establishes a versatile and highly efficient framework for enhanced electron beam control in the context of laser phase plates22, beam modulators and continuous-wave attosecond pulse trains23, resonantly enhanced spectroscopy24,25,26 and dielectric laser acceleration19,20,27. Our work introduces a universal platform for exploring free-electron quantum optics28,29,30,31, with potential future developments in strong coupling, local quantum probing and electron–photon entanglement

Involutory reflection groups and their models

AbstractA finite subgroup G of GL(n,C) is involutory if the sum of the dimensions of its irreducible complex representations is given by the number of absolute involutions in the group, i.e. elements g∈G such that gg¯=1, where the bar denotes complex conjugation. A uniform combinatorial model is constructed for all non-exceptional irreducible complex reflection groups which are involutory including, in particular, all infinite families of finite irreducible Coxeter groups

Predicting Crappie Recruitment in Ohio Reservoirs with Spawning Stock Size, Larval Density, and Chlorophyll Concentrations

Stock-recruit models typically use only spawning stock size as a predictor of recruitment to a fishery. In this paper, however, we used spawning stock size as well as larval density and key environmental variables to predict recruitment of white crappies Pomoxis annularis and black crappies P. nigromaculatus, a genus notorious for variable recruitment. We sampled adults and recruits from 11 Ohio reservoirs and larvae from 9 reservoirs during 1998-2001. We sampled chlorophyll as an index of reservoir productivity and obtained daily estimates of water elevation to determine the impact of hydrology on recruitment. Akaike's information criterion (AIC) revealed that Ricker and Beverton-Holt stock-recruit models that included chlorophyll best explained the variation in larval density and age-2 recruits. Specifically, spawning stock catch per effort (CPE) and chlorophyll explained 63-64% of the variation in larval density. In turn, larval density and chlorophyll explained 43-49% of the variation in age-2 recruit CPE. Finally, spawning stock CPE and chlorophyll were the best predictors of recruit CPE (i.e., 74-86%). Although larval density and recruitment increased with chlorophyll, neither was related to seasonal water elevation. Also, the AIC generally did not distinguish between Ricker and Beverton-Holt models. From these relationships, we concluded that crappie recruitment can be limited by spawning stock CPE and larval production when spawning stock sizes are low (i.e., CPE , 5 crappies/net-night). At higher levels of spawning stock sizes, spawning stock CPE and recruitment were less clearly related. To predict recruitment in Ohio reservoirs, managers should assess spawning stock CPE with trap nets and estimate chlorophyll concentrations. To increase crappie recruitment in reservoirs where recruitment is consistently poor, managers should use regulations to increase spawning stock size, which, in turn, should increase larval production and recruits to the fishery.This research was funded by Federal Aid in Sport Fish Restoration Project F-69-P, administered jointly by the U.S. Fish and Wildlife Service and Ohio Department of Natural Resources, Division of Wildlife, and the Department of Evolution, Ecology, and Organismal Biology at Ohio State University

Colored Motifs Reveal Computational Building Blocks in the C. elegans Brain

Background: Complex networks can often be decomposed into less complex sub-networks whose structures can give hints about the functional organization of the network as a whole. However, these structural motifs can only tell one part of the functional story because in this analysis each node and edge is treated on an equal footing. In real networks, two motifs that are topologically identical but whose nodes perform very different functions will play very different roles in the network. Methodology/Principal Findings: Here, we combine structural information derived from the topology of the neuronal network of the nematode C. elegans with information about the biological function of these nodes, thus coloring nodes by function. We discover that particular colorations of motifs are significantly more abundant in the worm brain than expected by chance, and have particular computational functions that emphasize the feed-forward structure of information processing in the network, while evading feedback loops. Interneurons are strongly over-represented among the common motifs, supporting the notion that these motifs process and transduce the information from the sensor neurons towards the muscles. Some of the most common motifs identified in the search for significant colored motifs play a crucial role in the system of neurons controlling the worm's locomotion. Conclusions/Significance: The analysis of complex networks in terms of colored motifs combines two independent data sets to generate insight about these networks that cannot be obtained with either data set alone. The method is general and should allow a decomposition of any complex networks into its functional (rather than topological) motifs as long as both wiring and functional information is available