Microbial Air Contamination in an Intensive Care Unit

Unit layout affects every aspect of intensive care services, including patient safety. A previous study has shown that patients admitted to beds adjacent to the sink and to the door of a large bayroom had the highest number of positive blood cultures and the highest blood culture incidence density, respectively. The present study measures microbial air contamination in a medical intensive care unit of a medical center in central Taiwan. Of the 17 rooms, 8 rooms with distinct physical environmental characteristics were selected. Sampling tests were conducted between December 2013 and February 2014 with a microbial air sampler (MAS-100NT). TSA was used for bacteria collection and DG18 for fungi collection. The overall average bacterial and fungal concentrations were 83CFU/m3 and 69CFU/m3, respectively. The ranges were between 8-354 CFU/m3 and 0-1468 CFU/m3, respectively. A significant difference was found in the bacterial concentration (p=.005) between different room locations. The highest concentration was found in the rooms located at the front end of the circulation (99 CFU/m3), while the lowest was found in the rooms located at the rear end of the circulation (55CFU/m3). Differences in fungal concentrations for different room locations did not reach statistical significance. In addition, differences in bacterial and fungal concentrations for rooms with different sink locations did not reach statistical significance. Even though the microbial concentrations generally complied with standards, the results may help designers and hospital administrators develop a healthier environment for patients

Atomic ionization by sterile-to-active neutrino conversion and constraints on dark matter sterile neutrinos with germanium detectors

The transition magnetic moment of a sterile-to-active neutrino conversion gives rise to not only radiative decay of a sterile neutrino, but also its non-standard interaction (NSI) with matter. For sterile neutrinos of keV-mass as dark matter candidates, their decay signals are actively searched for in cosmic X-ray spectra. In this work, we consider the NSI that leads to atomic ionization, which can be detected by direct dark matter experiments. It is found that this inelastic scattering process for a nonrelativistic sterile neutrino has a pronounced enhancement in the differential cross section at energy transfer about half of its mass, manifesting experimentally as peaks in the measurable energy spectra. The enhancement effects gradually smear out as the sterile neutrino becomes relativistic. Using data taken with germanium detectors that have fine energy resolution in keV and sub-keV regimes, constraints on sterile neutrino mass and its transition magnetic moment are derived and compared with those from astrophysical observations

MicroRNA-31 is required for astrocyte specification

Previously, we determined microRNA-31 (miR-31) is a noncoding tumor suppressive gene frequently deleted in glioblastoma (GBM); miR-31 suppresses tumor growth, in part, by limiting the activity of NF-κB. Herein, we expand our previous studies by characterizing the role of miR-31 during neural precursor cell (NPC) to astrocyte differentiation. We demonstrate that miR-31 expression and activity is suppressed in NPCs by stem cell factors such as Lin28, c-Myc, SOX2 and Oct4. However, during astrocytogenesis, miR-31 is induced by STAT3 and SMAD1/5/8, which mediate astrocyte differentiation. We determined miR-31 is required for terminal astrocyte differentiation, and that the loss of miR-31 impairs this process and/or prevents astrocyte maturation. We demonstrate that miR-31 promotes astrocyte development, in part, by reducing the levels of Lin28, a stem cell factor implicated in NPC renewal. These data suggest that miR-31 deletions may disrupt astrocyte development and/or homeostasis

PCA-based population structure inference with generic clustering algorithms

<p>Abstract</p> <p>Background</p> <p>Handling genotype data typed at hundreds of thousands of loci is very time-consuming and it is no exception for population structure inference. Therefore, we propose to apply PCA to the genotype data of a population, select the significant principal components using the Tracy-Widom distribution, and assign the individuals to one or more subpopulations using generic clustering algorithms.</p> <p>Results</p> <p>We investigated K-means, soft K-means and spectral clustering and made comparison to STRUCTURE, a model-based algorithm specifically designed for population structure inference. Moreover, we investigated methods for predicting the number of subpopulations in a population. The results on four simulated datasets and two real datasets indicate that our approach performs comparably well to STRUCTURE. For the simulated datasets, STRUCTURE and soft K-means with BIC produced identical predictions on the number of subpopulations. We also showed that, for real dataset, BIC is a better index than likelihood in predicting the number of subpopulations.</p> <p>Conclusion</p> <p>Our approach has the advantage of being fast and scalable, while STRUCTURE is very time-consuming because of the nature of MCMC in parameter estimation. Therefore, we suggest choosing the proper algorithm based on the application of population structure inference.</p

Subanesthetic ketamine treatment promotes abnormal interactions between neural subsystems and alters the properties of functional brain networks

Acute treatment with subanesthetic ketamine, a non-competitive N-methyl-D-aspartic acid (NMDA) receptor antagonist, is widely utilized as a translational model for schizophrenia. However, how acute NMDA receptor blockade impacts on brain functioning at a systems level, to elicit translationally relevant symptomatology and behavioral deficits, has not yet been determined. Here, for the first time, we apply established and recently validated topological measures from network science to brain imaging data gained from ketamine-treated mice to elucidate how acute NMDA receptor blockade impacts on the properties of functional brain networks. We show that the effects of acute ketamine treatment on the global properties of these networks are divergent from those widely reported in schizophrenia. Where acute NMDA receptor blockade promotes hyperconnectivity in functional brain networks, pronounced dysconnectivity is found in schizophrenia. We also show that acute ketamine treatment increases the connectivity and importance of prefrontal and thalamic brain regions in brain networks, a finding also divergent to alterations seen in schizophrenia. In addition, we characterize how ketamine impacts on bipartite functional interactions between neural subsystems. A key feature includes the enhancement of prefrontal cortex (PFC)-neuromodulatory subsystem connectivity in ketamine-treated animals, a finding consistent with the known effects of ketamine on PFC neurotransmitter levels. Overall, our data suggest that, at a systems level, acute ketamine-induced alterations in brain network connectivity do not parallel those seen in chronic schizophrenia. Hence, the mechanisms through which acute ketamine treatment induces translationally relevant symptomatology may differ from those in chronic schizophrenia. Future effort should therefore be dedicated to resolve the conflicting observations between this putative translational model and schizophrenia