Long-Term Structural Price Relationships in Real Estate Markets

This study investigates the long-run stochastic properties of real estate assets by geographical breakdown. We also study their linkages with financial assets. The initial tests find that almost all property types exhibit the presence of nonstationarity. Thus, cointegrated methodologies are used. Structural breakpoints identified in the literature are used as a guide to divide the data into two windows, 1983-1989 and 1990-1996. The results show that real estate in the different regions exhibit a closer relationship with each other in the second period, compared with the first. Also, strong linkages between real estate regions and financial assets are noted in the second period. The South is the only region to exhibit segmentation in both periods. Overall, the information derived from our analysis sheds light on linkages among real estate assets and between real estate and financial assets and also provides a framework for creating diversified portfolios.

Feasibility of Manual Teach-and-Replay and Continuous Impedance Shaping for Robotic Locomotor Training Following Spinal Cord Injury

Robotic gait training is an emerging technique for retraining walking ability following spinal cord injury (SCI). A key challenge in this training is determining an appropriate stepping trajectory and level of assistance for each patient, since patients have a wide range of sizes and impairment levels. Here, we demonstrate how a lightweight yet powerful robot can record subject-specific, trainer-induced leg trajectories during manually assisted stepping, then immediately replay those trajectories. Replay of the subject-specific trajectories reduced the effort required by the trainer during manual assistance, yet still generated similar patterns of muscle activation for six subjects with a chronic SCI. We also demonstrate how the impedance of the robot can be adjusted on a step-by-step basis with an error-based, learning law. This impedance-shaping algorithm adapted the robot's impedance so that the robot assisted only in the regions of the step trajectory where the subject consistently exhibited errors. The result was that the subjects stepped with greater variability, while still maintaining a physiologic gait pattern. These results are further steps toward tailoring robotic gait training to the needs of individual patients

Modelling the atomic structure of very high-density amorphous ice

The structure of very high-density amorphous (VHDA) ice has been modelled by positionally disordering three crystalline phases, namely ice IV, VI and XII. These phases were chosen because only they are stable or metastable in the region of the ice phase diagram where VHDA ice is formed, and their densities are comparable to that of VHDA ice. An excellent fit to the medium range of the experimentally observed pair-correlation function g(r) of VHDA ice was obtained by introducing disorder into the positions of the H2O molecules, as well as small amounts of molecular rotational disorder, disorder in the O--H bond lengths and disorder in the H--O--H bond angles. The low-k behaviour of the experimental structure factor, S(k), is also very well reproduced by this disordered-crystal model. The fraction of each phase present in the best-fit disordered model is very close to that observed in the probable crystallization products of VHDA ice. In particular, only negligible amounts of ice IV are predicted, in accordance with experimental observation.Comment: 4 pages, 3 figures, 1 table, v2: changes made in response to referees' comments, the justification for using certain ice phases is improved, and ice IV is now disordered as wel

Seasonal variation and impact of waste-water lagoons as larval habitat on the population dynamics of Culicoides sonorensis (Diptera:Ceratpogonidae) at two dairy farms in northern California.

The Sacramento (northern Central) Valley of California (CA) has a hot Mediterranean climate and a diverse ecological landscape that is impacted extensively by human activities, which include the intensive farming of crops and livestock. Waste-water ponds, marshes, and irrigated fields associated with these agricultural activities provide abundant larval habitats for C. sonorensis midges, in addition to those sites that exist in the natural environment. Within this region, C. sonorensis is an important vector of bluetongue (BTV) and related viruses that adversely affect the international trade and movement of livestock, the economics of livestock production, and animal welfare. To characterize the seasonal dynamics of immature and adult C. sonorensis populations, abundance was monitored intensively on two dairy farms in the Sacramento Valley from August 2012- to July 2013. Adults were sampled every two weeks for 52 weeks by trapping (CDC style traps without light and baited with dry-ice) along N-S and E-W transects on each farm. One farm had large operational waste-water lagoons, whereas the lagoon on the other farm was drained and remained dry during the study. Spring emergence and seasonal abundance of adult C. sonorensis on both farms coincided with rising vernal temperature. Paradoxically, the abundance of midges on the farm without a functioning waste-water lagoon was increased as compared to abundance on the farm with a waste-water lagoon system, indicating that this infrastructure may not serve as the sole, or even the primary larval habitat. Adult midges disappeared from both farms from late November until May; however, low numbers of parous female midges were detected in traps set during daylight in the inter-seasonal winter period. This latter finding is especially critical as it provides a potential mechanism for the "overwintering" of BTV in temperate regions such as northern CA. Precise documentation of temporal changes in the annual abundance and dispersal of Culicoides midges is essential for the creation of models to predict BTV infection of livestock and to develop sound abatement strategies

Non-contrast renal magnetic resonance imaging to assess perfusion and corticomedullary differentiation in health and chronic kidney disease

AIMS Arterial spin labelling (ASL) MRI measures perfusion without administration of contrast agent. While ASL has been validated in animals and healthy volunteers (HVs), application to chronic kidney disease (CKD) has been limited. We investigated the utility of ASL MRI in patients with CKD. METHODS We studied renal perfusion in 24 HVs and 17 patients with CKD (age 22-77 years, 40% male) using ASL MRI at 3.0T. Kidney function was determined using estimated glomerular filtration rate (eGFR). T1 relaxation time was measured using modified look-locker inversion and xFB02;ow-sensitive alternating inversion recovery true-fast imaging and steady precession was performed to measure cortical and whole kidney perfusion. RESULTS T1 was higher in CKD within cortex and whole kidney, and there was association between T1 time and eGFR. No association was seen between kidney size and volume and either T1, or ASL perfusion. Perfusion was lower in CKD in cortex (136 ± 37 vs. 279 ± 69 ml/min/100 g; p < 0.001) and whole kidney (146 ± 24 vs. 221 ± 38 ml/min/100 g; p < 0.001). There was significant, negative, association between T1 longitudinal relaxation time and ASL perfusion in both the cortex (r = -0.75, p < 0.001) and whole kidney (r = -0.50, p < 0.001). There was correlation between eGFR and both cortical (r = 0.73, p < 0.01) and whole kidney (r = 0.69, p < 0.01) perfusion. CONCLUSIONS Significant differences in renal structure and function were demonstrated using ASL MRI. T1 may be representative of structural changes associated with CKD; however, further investigation is required into the pathological correlates of reduced ASL perfusion and increased T1 time in CKD

Do changes in health reveal the possibility of undiagnosed pancreatic cancer? Development of a risk-prediction model based on healthcare claims data.

Background and objectiveEarly detection methods for pancreatic cancer are lacking. We aimed to develop a prediction model for pancreatic cancer based on changes in health captured by healthcare claims data.MethodsWe conducted a case-control study on 29,646 Medicare-enrolled patients aged 68 years and above with pancreatic ductal adenocarcinoma (PDAC) reported to the Surveillance Epidemiology an End Results (SEER) tumor registries program in 2004-2011 and 88,938 age and sex-matched controls. We developed a prediction model using multivariable logistic regression on Medicare claims for 16 risk factors and pre-diagnostic symptoms of PDAC present within 15 months prior to PDAC diagnosis. Claims within 3 months of PDAC diagnosis were excluded in sensitivity analyses. We evaluated the discriminatory power of the model with the area under the receiver operating curve (AUC) and performed cross-validation by bootstrapping.ResultsThe prediction model on all cases and controls reached AUC of 0.68. Excluding the final 3 months of claims lowered the AUC to 0.58. Among new-onset diabetes patients, the prediction model reached AUC of 0.73, which decreased to 0.63 when claims from the final 3 months were excluded. Performance measures of the prediction models was confirmed by internal validation using the bootstrap method.ConclusionModels based on healthcare claims for clinical risk factors, symptoms and signs of pancreatic cancer are limited in classifying those who go on to diagnosis of pancreatic cancer and those who do not, especially when excluding claims that immediately precede the diagnosis of PDAC

Opioid-related (ORL1) receptors are enriched in a subpopulation of sensory neurons and prolonged activation produces no functional loss of surface N-type calcium channels.

The opioid-related receptor, ORL1, is activated by the neuropeptide nociceptin/orphanin FQ (N/OFQ) and inhibits high-voltage-activated (HVA) calcium channel currents (I(Ca)) via a G-protein-coupled mechanism. Endocytosis of ORL1 receptor during prolonged N/OFQ exposure was proposed to cause N-type voltage-gated calcium channel (VGCC) internalization via physical interaction between ORL1 and the N-type channel. However, there is no direct electrophysiological evidence for this mechanism in dorsal root ganglion (DRG) neurons or their central nerve terminals. The present study tested this using whole-cell patch-clamp recordings of HVA I(Ca) in rat DRG neurons and primary afferent excitatory synaptic currents (eEPSCs) in spinal cord slices. DRG neurons were classified on the basis of diameter, isolectin-B4 (IB4) binding and responses to capsaicin, N/OFQ and a μ-opioid agonist, DAMGO. IB4-negative neurons less than 20 μm diameter were selectively responsive to N/OFQ as well as DAMGO. In these neurons, ORL1 desensitization by a supramaximal concentration of N/OFQ was not followed by a decrease in HVA I(Ca) current density or proportion of whole-cell HVA I(Ca) contributed by N-type VGCC as determined using the N-type channel selective blocker, ω-conotoxin CVID. There was also no decrease in the proportion of N-type I(Ca) when neurons were incubated at 37°C with N/OFQ for 30 min prior to recording. In spinal cord slices, N/OFQ consistently inhibited eEPSCs onto dorsal horn neurons. As observed in DRG neurons, preincubation of slices in N/OFQ for 30 min produced no decrease in the proportion of eEPSCs inhibited by CVID. In conclusion, no internalization of the N-type VGCC occurs in either the soma or central nerve terminals of DRG neurons following prolonged exposure to high, desensitizing concentrations of N/OFQ.NHMRC Grant: 056992

Do Lognormal Column-Density Distributions in Molecular Clouds Imply Supersonic Turbulence?

Recent observations of column densities in molecular clouds find lognormal distributions with power-law high-density tails. These results are often interpreted as indications that supersonic turbulence dominates the dynamics of the observed clouds. We calculate and present the column-density distributions of three clouds, modeled with very different techniques, none of which is dominated by supersonic turbulence. The first star-forming cloud is simulated using smoothed particle hydrodynamics (SPH); in this case gravity, opposed only by thermal-pressure forces, drives the evolution. The second cloud is magnetically subcritical with subsonic turbulence, simulated using nonideal MHD; in this case the evolution is due to gravitationally-driven ambipolar diffusion. The third cloud is isothermal, self-gravitating, and has a smooth density distribution analytically approximated with a uniform inner region and an r^-2 profile at larger radii. We show that in all three cases the column-density distributions are lognormal. Power-law tails develop only at late times (or, in the case of the smooth analytic profile, for strongly centrally concentrated configurations), when gravity dominates all opposing forces. It therefore follows that lognormal column-density distributions are generic features of diverse model clouds, and should not be interpreted as being a consequence of supersonic turbulence.Comment: 6 pages, 6 figures, accepted for publication in MNRA