Current driven electrostatic and electromagnetic ion cyclotron instabilities

Growth rates and parameter dependences are calculated for the current driven instabilities of electrostatic (with finite-beta corrections) and electromagnetic ion cyclotron waves. For 0.25 (T sub e)/(T sub i) 2.5, ion cyclotron waves have large growth rates, while ion acoustic waves are still stable. In fusion devices, where electrostatic waves may be stable, electromagnetic ion cyclotron waves are unstable for beta sub i 0.001

Influence of the Lower Hybrid Drift Instability on the onset of Magnetic Reconnection

Two-dimensional and three-dimensional kinetic simulation results reveal the importance of the Lower-Hybrid Drift Instability LHDI to the onset of magnetic reconnection. Both explicit and implicit kinetic simulations show that the LHDI heats electrons anisotropically and increases the peak current density. Linear theory predicts these modifications can increase the growth rate of the tearing instability by almost two orders of magnitude and shift the fastest growing modes to significantly shorter wavelengths. These predictions are confirmed by nonlinear kinetic simulations in which the growth and coalescence of small scale magnetic islands leads to a rapid onset of large scale reconnection

Antibiotic resistance genes in river biofilms: a metagenomic approach toward the identification of sources and candidate hosts

Treated wastewater is a major pathway by which antibiotic resistance genes (ARG) enter aquatic ecosystems. However, knowledge gaps remain concerning the dissemination of specific ARG and their association with bacterial hosts. Here, we employed shotgun metagenomics to track ARG and taxonomic markers in river biofilms along a gradient of fecal pollution depicted by crAssphage signatures. We found strong evidence for an impact of wastewater effluents on both community composition and resistomes. In the light of such simultaneity, we employed a model comparison technique to identify ARG-host relationships from nonassembled metagenomic DNA. Hereby, a major cause of spurious associations otherwise encountered in correlation-based ARG-host analyses was suppressed. For several families of ARG, namely those conferring resistance to beta-lactams, particular bacterial orders were identified as candidate hosts. The found associations of (bla)FOX and (cpf)A with Aeromonadales or (bla)PER with Chromatiales support the outcome of independent evolutionary analyses and thus confirm the potential of the methodology. For other ARG families including (bla)IMP or (tet), clusters of bacterial orders were identified which potentially harbor a major proportion of host species. For yet other ARG, like, for example, ant or erm, no particular host candidates were identifiable, indicating their spread across various taxonomic groups

Modeling emergency department visit patterns for infectious disease complaints: results and application to disease surveillance

BACKGROUND: Concern over bio-terrorism has led to recognition that traditional public health surveillance for specific conditions is unlikely to provide timely indication of some disease outbreaks, either naturally occurring or induced by a bioweapon. In non-traditional surveillance, the use of health care resources are monitored in "near real" time for the first signs of an outbreak, such as increases in emergency department (ED) visits for respiratory, gastrointestinal or neurological chief complaints (CC). METHODS: We collected ED CCs from 2/1/94 – 5/31/02 as a training set. A first-order model was developed for each of seven CC categories by accounting for long-term, day-of-week, and seasonal effects. We assessed predictive performance on subsequent data from 6/1/02 – 5/31/03, compared CC counts to predictions and confidence limits, and identified anomalies (simulated and real). RESULTS: Each CC category exhibited significant day-of-week differences. For most categories, counts peaked on Monday. There were seasonal cycles in both respiratory and undifferentiated infection complaints and the season-to-season variability in peak date was summarized using a hierarchical model. For example, the average peak date for respiratory complaints was January 22, with a season-to-season standard deviation of 12 days. This season-to-season variation makes it challenging to predict respiratory CCs so we focused our effort and discussion on prediction performance for this difficult category. Total ED visits increased over the study period by 4%, but respiratory complaints decreased by roughly 20%, illustrating that long-term averages in the data set need not reflect future behavior in data subsets. CONCLUSION: We found that ED CCs provided timely indicators for outbreaks. Our approach led to successful identification of a respiratory outbreak one-to-two weeks in advance of reports from the state-wide sentinel flu surveillance and of a reported increase in positive laboratory test results

Computational 3-D inversion for seismic exploration

This is the final report of a four-month, Laboratory Directed Research and Development (LDRD) project carried out at the Los Alamos National Laboratory (LANL). There is a great need for a new and effective technology with a wide scope of industrial applications to investigate media internal properties of which can be explored only from the backscattered data. The project was dedicated to the development of a three-dimensional computational inversion tool for seismic exploration. The new computational concept of the inversion algorithm was suggested. The goal of the project was to prove the concept and the practical validity of the algorithm for petroleum exploration