Season of the year influences infection rates following total hip arthroplasty

To research the influence of season of the year on periprosthetic joint infections. METHODS We conducted a retrospective review of the entire Medicare files from 2005 to 2014. Seasons were classified as spring, summer, fall or winter. Regional variations were accounted for by dividing patients into four geographic regions as per the United States Census Bureau (Northeast, Midwest, West and South). Acute postoperative infection and deep periprosthetic infections within 90 d after surgery were tracked. RESULTS In all regions, winter had the highest incidence of periprosthetic infections (mean 0.98%, SD 0.1%) and was significantly higher than other seasons in the Midwest, South and West (P \u3c 0.05 for all) but not the Northeast (P = 0.358). Acute postoperative infection rates were more frequent in the summer and were significantly affected by season of the year in the West. CONCLUSION Season of the year is a risk factor for periprosthetic joint infection following total hip arthroplasty (THA). Understanding the influence of season on outcomes following THA is essential when risk-stratifying patients to optimize outcomes and reduce episode of care costs. © The Author(s) 2017

Mobile radio interferometric geodetic systems

Operation of the Astronomical Radio Interferometric Earth Surveying (ARIES) in a proof of concept mode is discussed. Accuracy demonstrations over a short baseline, a 180 km baseline, and a 380 km baseline are documented. Use of ARIES in the Sea Slope Experiment of the National Geodetic Survey to study the apparent differences between oceanographic and geodetic leveling determinations of the sea surface along the Pacific Coast is described. Intergration of the NAVSTAR Global Positioning System and a concept called SERIES (Satellite Emission Radio Interferometric Earth Surveying) is briefly reviewed

Strongly Coupled Matter-Field and Non-Analytic Decay Rate of Dipole Molecules in a Waveguide

The decay rate \gam of an excited dipole molecule inside a waveguide is evaluated for the strongly coupled matter-field case near a cutoff frequency \ome_c without using perturbation analysis. Due to the singularity in the density of photon states at the cutoff frequency, we find that \gam depends non-analytically on the coupling constant $\ggg$ as $\ggg^{4/3}$. In contrast to the ordinary evaluation of \gam which relies on the Fermi golden rule (itself based on perturbation analysis), \gam has an upper bound and does not diverge at \ome_c even if we assume perfect conductance in the waveguide walls. As a result, again in contrast to the statement found in the literature, the speed of emitted light from the molecule does not vanish at \ome_c and is proportional to $c\ggg^{2/3}$ which is on the order of $10^3 \sim 10^4$ m/s for typical dipole molecules.Comment: 4 pages, 2 figure

Ryegrass ASTRA: a Web-Based Resource for \u3cem\u3eLolium\u3c/em\u3e EST Analysis

Perennial ryegrass (Lolium perenne L.) is a major grass species of temperate pastoral agriculture

Clover ASTRA: a Web-Based Resource for Trifolium EST Analysis

White clover (Trifolium repens L.) is a major temperate forage legume

Sum rule for the optical Hall angle

We consider the optical Hall conductivity of a general electronic medium and prove that the optical Hall angle obeys a new sum rule. This sum rule governs the response of an electronic fluid to a Lorentz electric field and can thought of as the transverse counterpart to the f-sum rule in optical conductivity. The physical meaning of this sum rule is discussed, giving a number of examples of its application to a variety of of electronic media.Comment: Four pages. Latex file with two postscript figure

Umklapp scattering from spin fluctuations in Copper-Oxides

The $\mathbf k$-dependent electronic momentum relaxation rate due to Umklapp scattering from antiferromagnetic spin fluctuations is studied within a renormalized mean-field approach to an extended $t-J$ model appropriate to YBa$_2$Cu$_3$O$_{7-x}$ and other cuprates. Transport coefficients are calculated in a relaxation time approximation. We compare these results with those obtained with the phenomenological assumption that all scattering processes dissipate momentum. We show that the latter, which violates momentum conservation, leads to quite different magnitudes and temperature dependences of resistivities and Hall coefficients.Comment: replaced by LaTeX file (due to problems with PostScript

Transgenesis and Genomics in Molecular Breeding of Forage Plants

Forage plant breeding has been largely based on phenotypic selection following sexual recombination of natural genetic variation found between and within ecotypes. Advances in plant genetic manipulation over the last 15 years have provided convincing evidence that these powerful technologies can complement and enhance plant breeding programs. Significant progress in the establishment of the methodologies required for the molecular breeding of forage plants has been made. Examples of current products and approaches for the application of these methodologies to forage grass and legume improvement are outlined. Large-scale genomic analysis of many organisms is under way with human, arabidopsis and rice genome sequences almost completed. Forage plant breeding is just now entering the genome era. The plethora of new technologies and tools now available for high-throughput gene discovery and genome-wide gene expression analysis have opened up opportunities for innovative applications in the identification, functional characterisation and use of genes of value in forage production systems and beyond. Examples of these opportunities, such as ‘molecular phenotyping’, ‘symbio-genomics’ and ‘xeno-genomics’ are introduced

Efficient Model Learning for Human-Robot Collaborative Tasks

We present a framework for learning human user models from joint-action demonstrations that enables the robot to compute a robust policy for a collaborative task with a human. The learning takes place completely automatically, without any human intervention. First, we describe the clustering of demonstrated action sequences into different human types using an unsupervised learning algorithm. These demonstrated sequences are also used by the robot to learn a reward function that is representative for each type, through the employment of an inverse reinforcement learning algorithm. The learned model is then used as part of a Mixed Observability Markov Decision Process formulation, wherein the human type is a partially observable variable. With this framework, we can infer, either offline or online, the human type of a new user that was not included in the training set, and can compute a policy for the robot that will be aligned to the preference of this new user and will be robust to deviations of the human actions from prior demonstrations. Finally we validate the approach using data collected in human subject experiments, and conduct proof-of-concept demonstrations in which a person performs a collaborative task with a small industrial robot