Forecasting Government Bond Yields with Neural Networks Considering Cointegration

This paper discusses techniques that might be helpful in predicting interest rates and tries to evaluate a new hybrid forecasting approach. Results of examining government bond yields in Germany and France reported in this study indicate that a hybrid forecasting approach which combines techniques of cointegration analysis with neural network (NN) forecasting models can produce superior results to the use of NN forecasting models alone. The findings documented in this paper could be a consequence of the fact that examining differenced data under certain conditions will lead to a loss of information and that the inclusion of the error correction term from the cointegration model can help to cope with this problem. The paper also discusses some possibly interesting directions for further research. Copyright © 2015 John Wiley & Sons, Ltd.     

An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting

For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive multiscale ensemble learning paradigm incorporating ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO) and least square support vector machines (LSSVM) with kernel function prototype is developed. Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine‐to‐coarse reconstruction algorithm, the high‐frequency, low‐frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high‐frequency components. LSSVM is suitable for forecasting the low‐frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. Copyright © 2016 John Wiley & Sons, Ltd.     

Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies

Genome-wide association is a promising approach to identify common genetic variants that predispose to human disease. Because of the high cost of genotyping hundreds of thousands of markers on thousands of subjects, genome-wide association studies often follow a staged design in which a proportion (pi(samples)) of the available samples are genotyped on a large number of markers in stage 1, and a proportion (pi(samples)) of these markers are later followed up by genotyping them on the remaining samples in stage 2. The standard strategy for analyzing such two-stage data is to view stage 2 as a replication study and focus on findings that reach statistical significance when stage 2 data are considered alone. We demonstrate that the alternative strategy of jointly analyzing the data from both stages almost always results in increased power to detect genetic association, despite the need to use more stringent significance levels, even when effect sizes differ between the two stages. We recommend joint analysis for all two-stage genome-wide association studies, especially when a relatively large proportion of the samples are genotyped in stage 1 (pi(samples) >or= 0.30), and a relatively large proportion of markers are selected for follow-up in stage 2 (pi(markers) >or= 0.01).     

Mutations in myosin heavy chain 11 cause a syndrome associating thoracic aortic aneurysm/aortic dissection and patent ductus arteriosus

We have recently described two kindreds presenting thoracic aortic aneurysm and/or aortic dissection (TAAD) and patent ductus arteriosus (PDA) and mapped the disease locus to 16p12.2-p13.13 (ref. 3). We now demonstrate that the disease is caused by mutations in the MYH11 gene affecting the C-terminal coiled-coil region of the smooth muscle myosin heavy chain, a specific contractile protein of smooth muscle cells (SMC). All individuals bearing the heterozygous mutations, even if asymptomatic, showed marked aortic stiffness. Examination of pathological aortas showed large areas of medial degeneration with very low SMC content. Abnormal immunological recognition of SM-MHC and the colocalization of wild-type and mutant rod proteins in SMC, in conjunction with differences in their coimmunoprecipitation capacities, strongly suggest a dominant-negative effect. Human MYH11 gene mutations provide the first example of a direct change in a specific SMC protein leading to an inherited arterial disease.     

High-throughput sequencing provides insights into genome variation and evolution in Salmonella Typhi

Isolates of Salmonella enterica serovar Typhi (Typhi), a human-restricted bacterial pathogen that causes typhoid, show limited genetic variation. We generated whole-genome sequences for 19 Typhi isolates using 454 (Roche) and Solexa (Illumina) technologies. Isolates, including the previously sequenced CT18 and Ty2 isolates, were selected to represent major nodes in the phylogenetic tree. Comparative analysis showed little evidence of purifying selection, antigenic variation or recombination between isolates. Rather, evolution in the Typhi population seems to be characterized by ongoing loss of gene function, consistent with a small effective population size. The lack of evidence for antigenic variation driven by immune selection is in contrast to strong adaptive selection for mutations conferring antibiotic resistance in Typhi. The observed patterns of genetic isolation and drift are consistent with the proposed key role of asymptomatic carriers of Typhi as the main reservoir of this pathogen, highlighting the need for identification and treatment of carriers.     

Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes

Genome-wide association (GWA) studies have identified multiple loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D). Established associations to common and rare variants explain only a small proportion of the heritability of T2D. As previously published analyses had limited power to identify variants with modest effects, we carried out meta-analysis of three T2D GWA scans comprising 10,128 individuals of European descent and approximately 2.2 million SNPs (directly genotyped and imputed), followed by replication testing in an independent sample with an effective sample size of up to 53,975. We detected at least six previously unknown loci with robust evidence for association, including the JAZF1 (P = 5.0 x 10(-14)), CDC123-CAMK1D (P = 1.2 x 10(-10)), TSPAN8-LGR5 (P = 1.1 x 10(-9)), THADA (P = 1.1 x 10(-9)), ADAMTS9 (P = 1.2 x 10(-8)) and NOTCH2 (P = 4.1 x 10(-8)) gene regions. Our results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D.     

Retinoic acid regulates avian lung branching through a molecular network

Retinoic acid (RA) is of major importance during vertebrate embryonic development and its levels need to be strictly regulated otherwise congenital malformations will develop. Through the action of specific nuclear receptors, named RAR/RXR, RA regulates the expression of genes that eventually influence proliferation and tissue patterning. RA has been described as crucial for different stages of mammalian lung morphogenesis, and as part of a complex molecular network that contributes to precise organogenesis; nonetheless, nothing is known about its role in avian lung development. The current report characterizes, for the first time, the expression pattern of RA signaling members (stra6, raldh2, raldh3, cyp26a1, rarα, and rarβ) and potential RA downstream targets (sox2, sox9, meis1, meis2, tgfβ2, and id2) by in situ hybridization. In the attempt of unveiling the role of RA in chick lung branching, in vitro lung explants were performed. Supplementation studies revealed that RA stimulates lung branching in a dose-dependent manner. Moreover, the expression levels of cyp26a1, sox2, sox9, rarβ, meis2, hoxb5, tgfβ2, id2, fgf10, fgfr2, and shh were evaluated after RA treatment to disclose a putative molecular network underlying RA effect. In situ hybridization analysis showed that RA is able to alter cyp26a1, sox9, tgfβ2, and id2 spatial distribution; to increase rarβ, meis2, and hoxb5 expression levels; and has a very modest effect on sox2, fgf10, fgfr2, and shh expression levels. Overall, these findings support a role for RA in the proximal–distal patterning and branching morphogenesis of the avian lung and reveal intricate molecular interactions that ultimately orchestrate branching morphogenesis.     

The impact of parameter and model uncertainty on market risk predictions from GARCH‐type models

We study the effect of parameter and model uncertainty on the left‐tail of predictive densities and in particular on VaR forecasts. To this end, we evaluate the predictive performance of several GARCH‐type models estimated via Bayesian and maximum likelihood techniques. In addition to individual models, several combination methods are considered, such as Bayesian model averaging and (censored) optimal pooling for linear, log or beta linear pools. Daily returns for a set of stock market indexes are predicted over about 13 years from the early 2000s. We find that Bayesian predictive densities improve the VaR backtest at the 1% risk level for single models and for linear and log pools. We also find that the robust VaR backtest exhibited by linear and log pools is better than the backtest of single models at the 5% risk level. Finally, the equally weighted linear pool of Bayesian predictives tends to be the best VaR forecaster in a set of 42 forecasting techniques.     

Massive glycosaminoglycan-dependent entry of Trp-containing cell-penetrating peptides induced by exogenous sphingomyelinase or cholesterol depletion

Among non-invasive cell delivery strategies, cell-penetrating peptide (CPP) vectors represent interesting new tools. To get fundamental knowledge about the still debated internalisation mechanisms of these peptides, we modified the membrane content of cells, typically by hydrolysis of sphingomyelin or depletion of cholesterol from the membrane outer leaflet. We quantified and visualised the effect of these viable cell surface treatments on the internalisation efficiency of different CPPs, among which the most studied Tat, R₉, penetratin and analogues, that all carry the N-terminal biotin-Gly₄ tag cargo. Under these cell membrane treatments, only penetratin and R₆W₃ underwent a massive glycosaminoglycan (GAG)-dependent entry in cells. Internalisation of the other peptides was only slightly increased, similarly in the absence or the presence of GAGs for R₉, and only in the presence of GAGs for Tat and R₆L₃. Ceramide formation (or cholesterol depletion) is known to lead to the reorganisation of membrane lipid domains into larger platforms, which can serve as a trap and cluster receptors. These results show that GAG clustering, enhanced by formation of ceramide, is efficiently exploited by penetratin and R₆W₃, which contains Trp residues in their sequence but not Tat, R₉ and R₆L₃. Hence, these data shed new lights on the differences in the internalisation mechanism and pathway of these peptides that are widely used in delivery of cargo molecules.     

Cross‐Section Stock Return and Implied Covariance between Jump and Diffusive Volatility

I examine the information content of option‐implied covariance between jumps and diffusive risk in the cross‐sectional variation in future returns. This paper documents that the difference between realized volatility and implied covariance (RV‐ICov) can predict future returns. The results show a significant and negative association of expected return and realized volatility–implied covariance spread in both the portfolio level analysis and cross‐sectional regression study. A trading strategy of buying a portfolio with the lowest RV‐ICov quintile portfolio and selling with the highest one generates positive and significant returns. This RV‐Cov anomaly is robust to controlling for size, book‐to‐market value, liquidity and systematic risk proportion. Copyright © 2015 John Wiley & Sons, Ltd.