Factors associated with Staphylococcus aureus nasal carriage among healthy people in Northern China

AbstractThere is still limited knowledge about the prevalence and risk factors of nasal carriage for Staphylococcus aureus among healthy carriers in China. We investigated 2448 healthy adults (≥18 years of age) from Beijing (n = 1530) and Harbin (n = 918) by nasal screening. Participants were checked for carriage of S. aureus, and health-related and demographic information between 2009 and 2011 was gathered. A total of 403 S. aureus (403/2448, 16.5%) were recovered, 8 of which were methicillin resistant (8/2448, 0.33%). Three factors were independently associated with S. aureus nasal carriage: Harbin as city of residence (odds ratio (OR) = 2.0, 95% confidence interval (CI) = 1.41 to 2.85), age ≤24 years (OR = 1.77, 95% CI = 1.30–2.44) and non-Han ethnicity (OR = 1.58, 95% CI = 1.05 to 2.38). On the basis of population genetic analysis using multiple locus variable number of tandem repeats analysis (MLVA) and spa typing, MLVA complex (MC) 398 and MC5a were the most prevalent clonal lineages in this collection. In multivariate models, residing in Harbin (OR = 1.77, 95% CI = 1.07–2.92) and having household members in the healthcare profession (OR = 3.69, 95% CI = 1.14–11.92) were factors associated with carriage of clonal lineage MC398. On the other hand, female sex (OR = 3.15, 95% CI = 1.35–7.33) and a history of chronic liver disease (OR = 16.93, 95% CI = 2.91–98.59) were associated with the clonal lineage MC5a. The three most common spa types were t571 (10.9%), t189 (9.9%) and t701 (7.2%). These findings provide insight into the determinants of nasal carriage and ecology for some of the most successful strains of S. aureus among healthy people in Northern China

Wavelet analysis of the transient QPOs in MAXI J1535−-571 with Insight-HXMT

Using wavelet analysis and power density spectrum, we investigate two transient quasi-periodic oscillations (QPOs) observed in MAXI J1535$-$571 observed with Insight-HXMT. The transient QPOs have a centroid frequency of $\sim 10$ Hz with a FWHM $\sim 0.6$ Hz and an rms amplitude $\sim 14\%$. Energy spectra of QPO and non-QPO regimes are also separated and analyzed, and the spectra become softer with higher $E_{cut}$ in the non-QPO regime compared to the QPO regime. Our results suggest that the transient QPOs detected in MJD 58016 and 58017 are still the type-C QPO, and the source remains in its HIMS. The duration of all type-C QPO signals based on wavelet is positively correlated with the mean count rate above $\sim 10$ keV, implying appearance of QPOs in different time scales should be coupled with the corona. The transient QPO properties could be related to the jet or flares, perhaps the partial ejection of the corona is responsible for the disappearance of the type-C QPO.Comment: 10 pages, MNRAS in pres

Dynamical Dark Energy or Simply Cosmic Curvature?

We show that the assumption of a flat universe induces critically large errors in reconstructing the dark energy equation of state at z>~0.9 even if the true cosmic curvature is very small, O(1%) or less. The spuriously reconstructed w(z) shows a range of unusual behaviour, including crossing of the phantom divide and mimicking of standard tracking quintessence models. For 1% curvature and LCDM, the error in w grows rapidly above z~0.9 reaching (50%,100%) by redshifts of (2.5,2.9) respectively, due to the long cosmological lever arm. Interestingly, the w(z) reconstructed from distance data and Hubble rate measurements have opposite trends due to the asymmetric influence of the curved geodesics. These results show that including curvature as a free parameter is imperative in any future analyses attempting to pin down the dynamics of dark energy, especially at moderate or high redshifts.Comment: 5 pages, 2 figures. To appear in JCA

How spiking neurons give rise to a temporal-feature map

A temporal-feature map is a topographic neuronal representation of temporal attributes of phenomena or objects that occur in the outside world. We explain the evolution of such maps by means of a spike-based Hebbian learning rule in conjunction with a presynaptically unspecific contribution in that, if a synapse changes, then all other synapses connected to the same axon change by a small fraction as well. The learning equation is solved for the case of an array of Poisson neurons. We discuss the evolution of a temporal-feature map and the synchronization of the single cells’ synaptic structures, in dependence upon the strength of presynaptic unspecific learning. We also give an upper bound for the magnitude of the presynaptic interaction by estimating its impact on the noise level of synaptic growth. Finally, we compare the results with those obtained from a learning equation for nonlinear neurons and show that synaptic structure formation may profit from the nonlinearity