Realization of a Resonant Fermi Gas with a Large Effective Range

We have measured the interaction energy and three-body recombination rate for a two-component Fermi gas near a narrow Feshbach resonance and found both to be strongly energy dependent. Even for deBroglie wavelengths greatly exceeding the van der Waals length scale, the behavior of the interaction energy as a function of temperature cannot be described by atoms interacting via a contact potential. Rather, energy-dependent corrections beyond the scattering length approximation are required, indicating a resonance with an anomalously large effective range. For fields where the molecular state is above threshold, the rate of three-body recombination is enhanced by a sharp, two-body resonance arising from the closed-channel molecular state which can be magnetically tuned through the continuum. This narrow resonance can be used to study strongly correlated Fermi gases that simultaneously have a sizeable effective range and a large scattering length.Comment: to appear in Phys. Rev. Let

Three-body recombination in a three-state Fermi gas with widely tunable interactions

We investigate the stability of a three spin state mixture of ultracold fermionic $^6$Li atoms over a range of magnetic fields encompassing three Feshbach resonances. For most field values, we attribute decay of the atomic population to three-body processes involving one atom from each spin state and find that the three-body loss coefficient varies by over four orders of magnitude. We observe high stability when at least two of the three scattering lengths are small, rapid loss near the Feshbach resonances, and two unexpected resonant loss features. At our highest fields, where all pairwise scattering lengths are approaching $a_t = -2140 a_0$, we measure a three-body loss coefficient $L_3 \simeq 5\times 10^{-22} \mathrm{cm}^6/\mathrm{s}$ and a trend toward lower decay rates for higher fields indicating that future studies of color superfluidity and trion formation in a SU(3) symmetric Fermi gas may be feasible

Relation between high-sensitivity C-reactive protein and cardiovascular and renal markers in a middle-income country in the African region.

BACKGROUND: High-sensitivity C-reactive protein (hs-CRP) is associated with several cardiovascular risk factors (CVRF) and with renal function markers. However, these associations have not been examined in populations in the African region. We analyzed the distribution of hs-CRP and the relationship with a broad set of CVRF, renal markers and carotid intima-media thickness (IMT), in the Seychelles (African region). METHODS: We conducted a survey in the population aged 25-64years (n=1255, participation rate: 80.2%). Analyses were restricted to persons of predominantly African descent (n=1011). RESULTS: Mean and median hs-CRP serum concentrations (mg/l) were 3.1 (SD 7.6) and 1.4 (IQR 0.7-2.9) in men and 4.5 (SD 6.7) and 2.2 (IQR 1.0-5.4) in women (p<0.001 for difference between men and women). hs-CRP was significantly associated with several conventional CVRF, and particularly strongly with markers of adiposity. With regards to renal markers, hs-CRP was strongly associated with cystatin C and with microalbuminuria but not with creatinine. hs-CRP was not associated with IMT. CONCLUSIONS: Serum concentration of hs-CRP was significantly associated with sex, several CVRF and selected renal function markers, which extends similar findings in Europe and in North America to a population in the African region. These findings can contribute to guide recommendations for the use of hs-CRP in clinical practice in the region

Folate catabolites in spot urine as non-invasive biomarkers of folate status during habitual intake and folic acid supplementation.

Folate status, as reflected by red blood cell (RCF) and plasma folates (PF), is related to health and disease risk. Folate degradation products para-aminobenzoylglutamate (pABG) and para-acetamidobenzoylglutamate (apABG) in 24 hour urine have recently been shown to correlate with blood folate. Since blood sampling and collection of 24 hour urine are cumbersome, we investigated whether the determination of urinary folate catabolites in fasted spot urine is a suitable non-invasive biomarker for folate status in subjects before and during folic acid supplementation. Immediate effects of oral folic acid bolus intake on urinary folate catabolites were assessed in a short-term pre-study. In the main study we included 53 healthy men. Of these, 29 were selected for a 12 week folic acid supplementation (400 µg). Blood, 24 hour and spot urine were collected at baseline and after 6 and 12 weeks and PF, RCF, urinary apABG and pABG were determined. Intake of a 400 µg folic acid bolus resulted in immediate increase of urinary catabolites. In the main study pABG and apABG concentrations in spot urine correlated well with their excretion in 24 hour urine. In healthy men consuming habitual diet, pABG showed closer correlation with PF (rs = 0.676) and RCF (rs = 0.649) than apABG (rs = 0.264, ns and 0.543). Supplementation led to significantly increased folate in plasma and red cells as well as elevated urinary folate catabolites, while only pABG correlated significantly with PF (rs = 0.574) after 12 weeks. Quantification of folate catabolites in fasted spot urine seems suitable as a non-invasive alternative to blood or 24 hour urine analysis for evaluation of folate status in populations consuming habitual diet. In non-steady-state conditions (folic acid supplementation) correlations between folate marker (RCF, PF, urinary catabolites) decrease due to differing kinetics

Assessing and Enabling Independent Component Analysis As A Hyperspectral Unmixing Approach

As a result of its capacity for material discrimination, hyperspectral imaging has been utilized for applications ranging from mining to agriculture to planetary exploration. One of the most common methods of exploiting hyperspectral images is spectral unmixing, which is used to discriminate and locate the various types of materials that are present in the scene. When this processing is done without the aid of a reference library of material spectra, the problem is called blind or unsupervised spectral unmixing. Independent component analysis (ICA) is a blind source separation approach that operates by finding outputs, called independent components, that are statistically independent. ICA has been applied to the unsupervised spectral unmixing problem, producing intriguing, if somewhat unsatisfying results. This dissatisfaction stems from the fact that independent components are subject to a scale ambiguity which must be resolved before they can be used effectively in the context of the spectral unmixing problem. In this dissertation, ICA is explored as a spectral unmixing approach. Various processing steps that are common in many ICA algorithms are examined to assess their impact on spectral unmixing results. Synthetically-generated but physically-realistic data are used to allow the assessment to be quantitative rather than qualitative only. Additionally, two algorithms, class-based abundance rescaling (CBAR) and extended class-based abundance rescaling (CBAR-X), are introduced to enable accurate rescaling of independent components. Experimental results demonstrate the improved rescaling accuracy provided by the CBAR and CBAR-X algorithms, as well as the general viability of ICA as a spectral unmixing approach

Development of an ex vivo porcine lung model for studying growth, virulence, and signaling of Pseudomonas aeruginosa

Research into chronic infection by bacterial pathogens, such as Pseudomonas aeruginosa, uses various in vitro and live host models. While these have increased our understanding of pathogen growth, virulence, and evolution, each model has certain limitations. In vitro models cannot recapitulate the complex spatial structure of host organs, while experiments on live hosts are limited in terms of sample size and infection duration for ethical reasons; live mammal models also require specialized facilities which are costly to run. To address this, we have developed an ex vivo pig lung (EVPL) model for quantifying Pseudomonas aeruginosa growth, quorum sensing (QS), virulence factor production, and tissue damage in an environment that mimics a chronically infected cystic fibrosis (CF) lung. In a first test of our model, we show that lasR mutants, which do not respond to 3-oxo-C12-homoserine lactone (HSL)-mediated QS, exhibit reduced virulence factor production in EVPL. We also show that lasR mutants grow as well as or better than a corresponding wild-type strain in EVPL. lasR mutants frequently and repeatedly arise during chronic CF lung infection, but the evolutionary forces governing their appearance and spread are not clear. Our data are not consistent with the hypothesis that lasR mutants act as social “cheats” in the lung; rather, our results support the hypothesis that lasR mutants are more adapted to the lung environment. More generally, this model will facilitate improved studies of mi- crobial disease, especially studies of how cells of the same and different species interact in polymicrobial infections in a spatially structured environment

Exact Hybrid Particle/Population Simulation of Rule-Based Models of Biochemical Systems

Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This "network-free" approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of "partial network expansion" into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility. © 2014 Hogg et al