A Search for Water in the Atmosphere of HAT-P-26b Using LDSS-3C

The characterization of a physically-diverse set of transiting exoplanets is an important and necessary step towards establishing the physical properties linked to the production of obscuring clouds or hazes. It is those planets with identifiable spectroscopic features that can most effectively enhance our understanding of atmospheric chemistry and metallicity. The newly-commissioned LDSS-3C instrument on Magellan provides enhanced sensitivity and suppressed fringing in the red optical, thus advancing the search for the spectroscopic signature of water in exoplanetary atmospheres from the ground. Using data acquired by LDSS-3C and the Spitzer Space Telescope, we search for evidence of water vapor in the transmission spectrum of the Neptune-mass planet HAT-P-26b. Our measured spectrum is best explained by the presence of water vapor, a lack of potassium, and either a high-metallicity, cloud-free atmosphere or a solar-metallicity atmosphere with a cloud deck at ~10 mbar. The emergence of multi-scale-height spectral features in our data suggests that future observations at higher precision could break this degeneracy and reveal the planet's atmospheric chemical abundances. We also update HAT-P-26b's transit ephemeris, t_0 = 2455304.65218(25) BJD_TDB, and orbital period, p = 4.2345023(7) days.Comment: 9 pages, 8 figures, Accepted for publication in Ap

Assessment of the effectiveness of head only and back-of-the-head electrical stunning of chickens

The study assesses the effectiveness of reversible head-only and back-of-the-head electrical stunning of chickens using 130–950 mA per bird at 50 Hz AC

Geography and Location Are the Primary Drivers of Office Microbiome Composition.

In the United States, humans spend the majority of their time indoors, where they are exposed to the microbiome of the built environment (BE) they inhabit. Despite the ubiquity of microbes in BEs and their potential impacts on health and building materials, basic questions about the microbiology of these environments remain unanswered. We present a study on the impacts of geography, material type, human interaction, location in a room, seasonal variation, and indoor and microenvironmental parameters on bacterial communities in offices. Our data elucidate several important features of microbial communities in BEs. First, under normal office environmental conditions, bacterial communities do not differ on the basis of surface material (e.g., ceiling tile or carpet) but do differ on the basis of the location in a room (e.g., ceiling or floor), two features that are often conflated but that we are able to separate here. We suspect that previous work showing differences in bacterial composition with surface material was likely detecting differences based on different usage patterns. Next, we find that offices have city-specific bacterial communities, such that we can accurately predict which city an office microbiome sample is derived from, but office-specific bacterial communities are less apparent. This differs from previous work, which has suggested office-specific compositions of bacterial communities. We again suspect that the difference from prior work arises from different usage patterns. As has been previously shown, we observe that human skin contributes heavily to the composition of BE surfaces. IMPORTANCE Our study highlights several points that should impact the design of future studies of the microbiology of BEs. First, projects tracking changes in BE bacterial communities should focus sampling efforts on surveying different locations in offices and in different cities but not necessarily different materials or different offices in the same city. Next, disturbance due to repeated sampling, though detectable, is small compared to that due to other variables, opening up a range of longitudinal study designs in the BE. Next, studies requiring more samples than can be sequenced on a single sequencing run (which is increasingly common) must control for run effects by including some of the same samples in all of the sequencing runs as technical replicates. Finally, detailed tracking of indoor and material environment covariates is likely not essential for BE microbiome studies, as the normal range of indoor environmental conditions is likely not large enough to impact bacterial communities

Accelerated Calvarial Healing in Mice Lacking Toll-Like Receptor 4

The bone and immune systems are closely interconnected. The immediate inflammatory response after fracture is known to trigger a healing cascade which plays an important role in bone repair. Toll-like receptor 4 (TLR4) is a member of a highly conserved receptor family and is a critical activator of the innate immune response after tissue injury. TLR4 signaling has been shown to regulate the systemic inflammatory response induced by exposed bone components during long-bone fracture. Here we tested the hypothesis that TLR4 activation affects the healing of calvarial defects. A 1.8 mm diameter calvarial defect was created in wild-type (WT) and TLR4 knockout (TLR4-/-) mice. Bone healing was tested using radiographic, histologic and gene expression analyses. Radiographic and histomorphometric analyses revealed that calvarial healing was accelerated in TLR4-/- mice. More bone was observed in TLR4-/- mice compared to WT mice at postoperative days 7 and 14, although comparable healing was achieved in both groups by day 21. Bone remodeling was detected in both groups on postoperative day 28. In TLR4-/- mice compared to WT mice, gene expression analysis revealed that higher expression levels of IL-1β, IL-6, TNF-α,TGF-β1, TGF-β3, PDGF and RANKL and lower expression level of RANK were detected at earlier time points (≤ postoperative 4 days); while higher expression levels of IL-1β and lower expression levels of VEGF, RANK, RANKL and OPG were detected at late time points (> postoperative 4 days). This study provides evidence of accelerated bone healing in TLR4-/- mice with earlier and higher expression of inflammatory cytokines and with increased osteoclastic activity. Further work is required to determine if this is due to inflammation driven by TLR4 activation. © 2012 Wang et al

A Practical Approach to the Assessment and Management of Psychiatric Emergencies

Patients present to the psychiatric emergency room manifesting a wide variety of symptoms ranging from mild anxiety to violent, uncontrollable behavior. The emergency room psychiatrist must recognize these various presentations and provide appropriate interventions for such patients. It may not be of the utmost importance to distinguish between the subtypes of schizophrenia or endogenous vs. exogenous depression, just as it is not crucial to decide immediately the cause of an acute abdomen, primarily to recognize its presence. However, it is critical to recognize the presence of a psychosis, suicidal intent or the presence of an organically caused alteration in mental status. These are, in essence triage decisions. In fact, the concordance rate between emergency room and final diagnoses other than alcoholism is no higher than 62% (1). According to Bassuk (2), the immediate goal of any emergency evaluation is to identify and manage life-threatening or potentially life-threatening problems, including out-of-control behaviors, serious and chronic self-neglect and severe medical problems either co-existing with or causing psychiatric symptoms. The psychiatrist must have acute management strategies and disposition decisions in mind so that within the initial phases of the interview, impressions regarding the need for restraint and/or psychoactive medication can be put into action. In less emergent situations or after appropriate measures have been taken to ensure safety and control, more time can be devoted toward a complete evaluation

Fitting parametric random effects models in very large data sets with application to VHA national data

<p>Abstract</p> <p>Background</p> <p>With the current focus on personalized medicine, patient/subject level inference is often of key interest in translational research. As a result, random effects models (REM) are becoming popular for patient level inference. However, for very large data sets that are characterized by large sample size, it can be difficult to fit REM using commonly available statistical software such as SAS since they require inordinate amounts of computer time and memory allocations beyond what are available preventing model convergence. For example, in a retrospective cohort study of over 800,000 Veterans with type 2 diabetes with longitudinal data over 5 years, fitting REM via generalized linear mixed modeling using currently available standard procedures in SAS (e.g. PROC GLIMMIX) was very difficult and same problems exist in Stata’s gllamm or R’s lme packages. Thus, this study proposes and assesses the performance of a meta regression approach and makes comparison with methods based on sampling of the full data.</p> <p>Data</p> <p>We use both simulated and real data from a national cohort of Veterans with type 2 diabetes (n=890,394) which was created by linking multiple patient and administrative files resulting in a cohort with longitudinal data collected over 5 years.</p> <p>Methods and results</p> <p>The outcome of interest was mean annual HbA1c measured over a 5 years period. Using this outcome, we compared parameter estimates from the proposed random effects meta regression (REMR) with estimates based on simple random sampling and VISN (Veterans Integrated Service Networks) based stratified sampling of the full data. Our results indicate that REMR provides parameter estimates that are less likely to be biased with tighter confidence intervals when the VISN level estimates are homogenous.</p> <p>Conclusion</p> <p>When the interest is to fit REM in repeated measures data with very large sample size, REMR can be used as a good alternative. It leads to reasonable inference for both Gaussian and non-Gaussian responses if parameter estimates are homogeneous across VISNs.</p