Bayes estimates of the cyclical component in twentieth centruy US gross domestic product

Cyclical components in economic time series are analysed in a Bayesian framework, thereby allowing prior notions about periodicity to be used. The method is based on a general class of unobserved component models that encompasses a range of dynamics in the stochastic cycle. This allows for instance relatively smooth cycles to be extracted from time series. Posterior densities of parameters and estimated components are obtained using Markov chain Monte Carlo methods, which we develop for both univariate and multivariate models. Features such as time-varying amplitude may be studied by examining different functions of the posterior draws for the cyclical component and parameters. The empirical application illustrates the method for annual US real GDP over the last 130 years

Trends and cycles in economic time series: A Bayesian approach

Trends and cyclical components in economic time series are modeled in a Bayesian framework. This enables prior notions about the duration of cycles to be used, while the generalized class of stochastic cycles employed allows the possibility of relatively smooth cycles being extracted. The posterior distributions of such underlying cycles can be very informative for policy makers, particularly with regard to the size and direction of the output gap and potential turning points. From the technical point of view a contribution is made in investigating the most appropriate prior distributions for the parameters in the cyclical components and in developing Markov chain Monte Carlo methods for both univariate and multivariate models. Applications to US macroeconomic series are presented

Investigations into the function and chemical compositions of the porose areas secretion of Rhipicephalus evertsi evertsi during oviposition

Major differences were observed in hexane, ethanol and butanol extracts of eggs obtained from Rhipicephillus evertsi evertsi females in which the porose areas functioned normally (AP⁺ eggs) and from R. evertsi evertsi in which the porose areas were selectively destroyed by electrocautery (APˉeggs). Mass yields and UV spectra of the hexane extracts were similar for AP⁺ and APˉeggs. The UV spectra changed only slightly in the 294-320 nm range with respect to time and temperature. High performance liquid chromatography revealed 2 components which originate from the porose areas. Mass spectroscopy of these components indicated the presence of aliphatic and phenolic groups. The ethanol and butanol extracts showed quantitative but no qualitative differences with respect to AP⁺ and APˉeggs. Electrophoretic fractionation of the butanol extracts revealed the presence of proteins in the secretion of the porose areas. Apart from this information on the chemical composition of the secretion, no indication was obtained of their function during oviposition of R. evertsi evertsi.The articles have been scanned in colour with a HP Scanjet 5590; 600dpi. Adobe Acrobat XI Pro was used to OCR the text and also for the merging and conversion to the final presentation PDF-format

Does hepatocellular carcinoma in non-alcoholic steatohepatitis exist in cirrhotic and non-cirrhotic patients?

Non-alcoholic steatohepatitis (NASH) has been associated with hepatocellular carcinoma (HCC) often arising in histologically advanced disease when steatohepatitis is not active (cryptogenic cirrhosis). Our objective was to characterize patients with HCC and active, histologically defined steatohepatitis. Among 394 patients with HCC detected by ultrasound imaging over 8 years and staged by the Barcelona Clinic Liver Cancer (BCLC) criteria, we identified 7 cases (1.7%) with HCC occurring in the setting of active biopsy-proven NASH. All were negative for other liver diseases such as hepatitis C, hepatitis B, autoimmune hepatitis, Wilson disease, and hemochromatosis. The patients (4 males and 3 females, age 63 ± 13 years) were either overweight (4) or obese (3); 57% were diabetic and 28.5% had dyslipidemia. Cirrhosis was present in 6 of 7 patients, but 1 patient had well-differentiated HCC in the setting of NASH without cirrhosis (fibrosis stage 1) based on repeated liver biopsies, the absence of portal hypertension by clinical and radiographic evaluations and by direct surgical inspection. Among the cirrhotic patients, 71.4% were clinically staged as Child A and 14.2% as Child B. Tumor size ranged from 1.0 to 5.2 cm and 5 of 7 patients were classified as early stage; 46% of all nodules were hyper-echoic and 57% were <3 cm. HCC was well differentiated in 1/6 and moderately differentiated in 5/6. Alpha-fetoprotein was <100 ng/mL in all patients. HCC in patients with active steatohepatitis is often multifocal, may precede clinically advanced disease and occurs without diagnostic levels of alpha-fetoprotein. Importantly, HCC may occur in NASH in the absence of cirrhosis. More aggressive screening of NASH patients may be warranted

Implementation of an Optimal First-Order Method for Strongly Convex Total Variation Regularization

We present a practical implementation of an optimal first-order method, due to Nesterov, for large-scale total variation regularization in tomographic reconstruction, image deblurring, etc. The algorithm applies to $\mu$-strongly convex objective functions with $L$-Lipschitz continuous gradient. In the framework of Nesterov both $\mu$ and $L$ are assumed known -- an assumption that is seldom satisfied in practice. We propose to incorporate mechanisms to estimate locally sufficient $\mu$ and $L$ during the iterations. The mechanisms also allow for the application to non-strongly convex functions. We discuss the iteration complexity of several first-order methods, including the proposed algorithm, and we use a 3D tomography problem to compare the performance of these methods. The results show that for ill-conditioned problems solved to high accuracy, the proposed method significantly outperforms state-of-the-art first-order methods, as also suggested by theoretical results.Comment: 23 pages, 4 figure

Blood-based metabolic signatures in Alzheimer's disease

Introduction Identification of blood-based metabolic changes might provide early and easy-to-obtain biomarkers. Methods We included 127 Alzheimer's disease (AD) patients and 121 control subjects with cerebrospinal fluid biomarker-confirmed diagnosis (cutoff tau/amyloid β peptide 42: 0.52). Mass spectrometry platforms determined the concentrations of 53 amine compounds, 22 organic acid compounds, 120 lipid compounds, and 40 oxidative stress compounds. Multiple signatures were assessed: differential expression (nested linear models), classification (logistic regression), and regulatory (network extraction). Results Twenty-six metabolites were differentially expressed. Metabolites improved the classification performance of clinical variables from 74% to 79%. Network models identified five hubs of metabolic dysregulation: tyrosine, glycylglycine, glutamine, lysophosphatic acid C18:2, and platelet-activating factor C16:0. The metabolite network for apolipoprotein E (APOE) ε4 negative AD patients was less cohesive compared with the network for APOE ε4 positive AD patients. Discussion Multiple signatures point to various promising peripheral markers for further validation. The network differences in AD patients according to APOE genotype may reflect different pathways to AD

Levonorgestrel-releasing intrauterine system versus endometrial ablation for heavy menstrual bleeding

Acknowledgments: We thank all the women who participated in this trial; the participating general practitioners, gynecologists, and hospitals; the research nurses; and the staff of the Dutch Consortium for Studies in Women’s Health and Reproduction.Peer reviewedPublisher PD