Wearable schizophrenia treatment with real-time affective context awareness

The Personal Health Assistant Project (PHA) is a pilot system implementation sponsored by the Kozani Region Governors’ Association (KRGA) and installed in one of the two major public hospitals of the city of Kozani. PHA is intended to demonstrate how a secure, networked, multipurpose electronic health and food benefits digital signage system can transform common TV sets inside patient homes or hospital rooms into health care media players and facilitate information sharing and improve administrative efficiency among private doctors, public health care providers, informal caregivers, and nutrition program private companies, while placing individual patients firmly in control of the information at hand. This case evaluation of the PHA demonstration is intended to provide critical information to other decision makers considering implementing PHA or related digital signage technology at other institutions and public hospitals around the globe.

Measurement of the cross-section for W boson production in association with b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

This paper reports a measurement of the W+b-jets (W+b+X and W+b (b) over bar +X) production cross-section in proton-proton collisions at a centre-of-mass energy of 7 TeV at the LHC. These results are based on data corresponding to an integrated luminosity of 4.6 fb(-1), collected with the ATLAS detector. Cross-sections are presented as a function of jet multiplicity and of the transverse momentum of the leading b-jet for both the muon and electron decay modes of the W boson. The W+b-jets cross-section, corrected for all known detector effects, is quoted in a limited kinematic range. Combining the muon and electron channels, the fiducial cross-section for W+b-jets is measured to be 7.1 +/- 0.5 (stat) +/- 1.4 (syst) pb, consistent with the next-to-leading order QCD prediction, corrected for non-perturbative and double-parton interactions (DPI) contributions, of 4.70 +/- 0.09 (stat) (+0.60)(-0.49) (scale) +/- 0.06 (PDF) +/- 0.16 (non-pert) (+0.52)(-0.38) (DPI) pb.

The effects of doxycycline on nitric oxide and stromelysin production in dogs with cranial cruciate ligament rupture

OBJECTIVE To investigate the potential of doxycycline to reduce stromelysin and inducible nitric oxide synthase (iNOS) activity in dogs with osteoarthritis (OA) secondary to spontaneous cranial cruciate ligament (CCL) rupture. STUDY DESIGN Prospective, clinical study. ANIMALS Eighty-one dogs with OA secondary to CCL rupture and 54 normal dogs. METHODS Dogs with OA secondary to CCL rupture were divided into 2 groups before surgery. The Doxy-CCl group received 3 to 4 mg/kg doxycycline orally every 24 hours for 7 to 10 days (n = 35). The CCL group received no treatment (n = 46). Synovial fluid, articular cartilage, synovial membrane, and CCL samples were collected during surgery (Doxy-CCL group and CCL group) or immediately after euthanasia from healthy dogs (control group). Synovial fluid samples were examined cytologically. Total nitric oxide (NOt) concentrations were measured in the supernatant of explant cultures of all tissue samples, and stromelysin activity was measured in the supernatant of explant cultures of cartilage. RESULTS NOt concentrations measured in cartilage were significantly lower in the Doxy-CCL group than in the CCL group, but were not different from those measured in the control group. Doxycycline treatment did not have a significant effect on cartilage stromelysin levels. CONCLUSION The findings in this study indicate that doxycycline inhibits NO production in cartilage in dogs with CCL rupture. CLINICAL RELEVANCE Doxycycline may have a role in the treatment of canine OA by inhibiting NO production.

Lepton Flavor Violation in the Standard Model with general Dimension-Six Operators

We study lepton flavor observables in the Standard Model (SM) extended with all dimension-6 operators which are invariant under the SM gauge group. We calculate the complete one-loop predictions to the radiative lepton decays μ → eγ, τ → μγ and τ → eγ as well as to the closely related anomalous magnetic moments and electric dipole moments of charged leptons, taking into account all dimension-6 operators which can generate lepton flavor violation. Also the 3-body flavor violating charged lepton decays τ ± → μ ± μ + μ −, τ ± → e ± e + e −, τ ± → e ± μ + μ −, τ ± → μ ± e + e −, τ ± → e ∓ μ ± μ ±, τ ± → μ ∓ e ± e ± and μ ± → e ± e + e − and the Z 0 decays Z 0 → ℓ+iℓ−j are considered, taking into account all tree-level contributions.

Past and future evolution of Abies alba forests in Europe - comparison of a dynamic vegetation model with palaeo data and observations

Information on how species distributions and ecosystem services are impacted by anthropogenic climate change is important for adaptation planning. Palaeo data suggest that Abies alba formed forests under significantly warmer-than-present conditions in Europe and might be a native substitute for widespread drought-sensitive temperate and boreal tree species such as beech (Fagus sylvatica) and spruce (Picea abies) under future global warming conditions. Here, we combine pollen and macrofossil data, modern observations, and results from transient simulations with the LPX-Bern dynamic global vegetation model to assess past and future distributions of A. alba in Europe. LPX-Bern is forced with climate anomalies from a run over the past 21 000 years with the Community Earth System Model, modern climatology, and with 21st-century multimodel ensemble results for the high-emission RCP8.5 and the stringent mitigation RCP2.6 pathway. The simulated distribution for present climate encompasses the modern range of A. alba, with the model exceeding the present distribution in north-western and southern Europe. Mid-Holocene pollen data and model results agree for southern Europe, suggesting that at present, human impacts suppress the distribution in southern Europe. Pollen and model results both show range expansion starting during the Bølling–Allerød warm period, interrupted by the Younger Dryas cold, and resuming during the Holocene. The distribution of A. alba expands to the north-east in all future scenarios, whereas the potential (currently unrealized) range would be substantially reduced in southern Europe under RCP8.5. A. alba maintains its current range in central Europe despite competition by other thermophilous tree species. Our combined palaeoecological and model evidence suggest that A. alba may ensure important ecosystem services including stand and slope stability, infrastructure protection, and carbon sequestration under significantly warmer-than-present conditions in central Europe.

Calibrating a CGE model with NTBs that Incorporates Standard Models of Modern Trade Theory

We propose a way to incorporate NTBs for the four workhorse models of the modern trade literature in computable general equilibrium models (CGEs). CGE models feature intermediate linkages and thus allow us to study global value chains (GVCs). We show that the Ethier-Krugman monopolistic competition model, the Melitz firm heterogeneity model and the Eaton and Kortum model can be defined as an Armington model with generalized marginal costs, generalized trade costs and a demand externality. As already known in the literature in both the Ethier-Krugman model and the Melitz model generalized marginal costs are a function of the amount of factor input bundles. In the Melitz model generalized marginal costs are also a function of the price of the factor input bundles. Lower factor prices raise the number of firms that can enter the market profitably (extensive margin), reducing generalized marginal costs of a representative firm. For the same reason the Melitz model features a demand externality: in a larger market more firms can enter. We implement the different models in a CGE setting with multiple sectors, intermediate linkages, non-homothetic preferences and detailed data on trade costs. We find the largest welfare effects from trade cost reductions in the Melitz model. We also employ the Melitz model to mimic changes in Non tariff Barriers (NTBs) with a fixed cost-character by analysing the effect of changes in fixed trade costs. While we work here with a model calibrated to the GTAP database, the methods developed can also be applied to CGE models based on the WIOD database.

Monetary Policy in a Portfolio Balance Model with Endogenous Physical Capital

We develop a portfolio balance model with real capital accumulation. The introduction of real capital as an asset as well as a good produced and demanded by firms enriches extant portfolio balance models of exchange rate determination. We show that expansionary monetary policy causes exchange rate overshooting, not once, but twice; the secondary repercussion comes through the reaction of firms to changed asset prices and the firms' decisions to invest in real capital. The model sheds further light on the volatility of real and nominal exchange rates, and it suggests that changes in corporate sector profitability may affect exchange rates through portfolio diversification in corporate securities.

An ordinal logistic regression model with misclassification of the outcome variable and categorical covariate

Ordinal outcomes are frequently employed in diagnosis and clinical trials. Clinical trials of Alzheimer's disease (AD) treatments are a case in point using the status of mild, moderate or severe disease as outcome measures. As in many other outcome oriented studies, the disease status may be misclassified. This study estimates the extent of misclassification in an ordinal outcome such as disease status. Also, this study estimates the extent of misclassification of a predictor variable such as genotype status. An ordinal logistic regression model is commonly used to model the relationship between disease status, the effect of treatment, and other predictive factors. A simulation study was done. First, data based on a set of hypothetical parameters and hypothetical rates of misclassification was created. Next, the maximum likelihood method was employed to generate likelihood equations accounting for misclassification. The Nelder-Mead Simplex method was used to solve for the misclassification and model parameters. Finally, this method was applied to an AD dataset to detect the amount of misclassification present. The estimates of the ordinal regression model parameters were close to the hypothetical parameters. β1 was hypothesized at 0.50 and the mean estimate was 0.488, β2 was hypothesized at 0.04 and the mean of the estimates was 0.04. Although the estimates for the rates of misclassification of X1 were not as close as β1 and β2, they validate this method. X 1 0-1 misclassification was hypothesized as 2.98% and the mean of the simulated estimates was 1.54% and, in the best case, the misclassification of k from high to medium was hypothesized at 4.87% and had a sample mean of 3.62%. In the AD dataset, the estimate for the odds ratio of X 1 of having both copies of the APOE 4 allele changed from an estimate of 1.377 to an estimate 1.418, demonstrating that the estimates of the odds ratio changed when the analysis includes adjustment for misclassification. ^

Concomitant Mycobacterium tuberculosis infection and type 2 diabetes: A descriptive model with global health implications

The type 2 diabetes (diabetes) pandemic is recognized as a threat to tuberculosis (TB) control worldwide. This secondary data analysis project estimated the contribution of diabetes to TB in a binational community on the Texas-Mexico border where both diseases occur. Newly-diagnosed TB patients > 20 years of age were prospectively enrolled at Texas-Mexico border clinics between January 2006 and November 2008. Upon enrollment, information regarding social, demographic, and medical risks for TB was collected at interview, including self-reported diabetes. In addition, self-reported diabetes was supported by blood-confirmation according to guidelines published by the American Diabetes Association (ADA). For this project, data was compared to existing statistics for TB incidence and diabetes prevalence from the corresponding general populations of each study site to estimate the relative and attributable risks of diabetes to TB. In concordance with historical sociodemographic data provided for TB patients with self-reported diabetes, our TB patients with diabetes also lacked the risk factors traditionally associated with TB (alcohol abuse, drug abuse, history of incarceration, and HIV infection); instead, the majority of our TB patients with diabetes were characterized by overweight/obesity, chronic hyperglycemia, and older median age. In addition, diabetes prevalence among our TB patients was significantly higher than in the corresponding general populations. Findings of this study will help accurately characterize TB patients with diabetes, thus aiding in the timely recognition and diagnosis of TB in a population not traditionally viewed as at-risk. We provide epidemiological and biological evidence that diabetes continues to be an increasingly important risk factor for TB.^

Bayesian estimation of multilevel item response model with missing data

This study proposed a novel statistical method that modeled the multiple outcomes and missing data process jointly using item response theory. This method follows the "intent-to-treat" principle in clinical trials and accounts for the correlation between outcomes and missing data process. This method may provide a good solution to chronic mental disorder study. ^ The simulation study demonstrated that if the true model is the proposed model with moderate or strong correlation, ignoring the within correlation may lead to overestimate of the treatment effect and result in more type I error than specified level. Even if the within correlation is small, the performance of proposed model is as good as naïve response model. Thus, the proposed model is robust for different correlation settings if the data is generated by the proposed model.^