Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models

In recent years fractionally differenced processes have received a great deal of attention due to its flexibility in financial applications with long memory. This paper considers a class of models generated by Gegenbauer polynomials, incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the statistical properties of the new model, suggest using the spectral likelihood estimation for long memory processes, and investigate the finite sample properties via Monte Carlo experiments. We apply the model to three exchange rate return series. Overall, the results of the out-of-sample forecasts show the adequacy of the new GLMSV model

Nonlinear time series and neural-network models of exchange rates between the US dollar and major currencies

This paper features an analysis of major currency exchange rate movements in relation to the US dollar, as constituted in US dollar terms. Euro, British pound, Chinese yuan, and Japanese yen are modelled using a variety of non- linear models, including smoot

Cointegrated Dynamics for A Generalized Long Memory Process

Recent developments in econometric methods enable estimation and testing of general long memory process, which include the general Gegenbauer process. This paper considers the error correction model for a vector general long memory process, which encompasses the vector autoregressive fractionally-integrated moving average and general Gegenbauer process. We modify the tests for unit roots and cointegration, based on the concept of heterogeneous autoregression. The Monte Carlo simulations show that the finite sample properties of the modified tests are satisfactory, while the conventional tests suffer from size distortion. Empirical results for interest rates series for the U.S.A. and Australia indicate that: (1) the modified unit root test detected unit roots for all series, (2) after differencing, all series favour the general Gegenbauer process, (3) the modified test for cointegration found only two cointegrating vectors, and (4) the zero interest rate policy in the U.S.A. has no effect on the cointegrating vector for the two countrie

Myocyte Enhancer Factor 2 and Class II Histone Deacetylases Control a Gender-Specific Pathway of Cardioprotection Mediated by the Estrogen Receptor

Gender differences in cardiovascular disease have long been recognized and attributed to beneficial cardiovascular actions of estrogen. Class II histone deacetylases (HDACs) act as key modulators of heart disease by repressing the activity of the myocyte enhancer factor (MEF)2 transcription factor, which promotes pathological cardiac remodeling in response to stress. Although it is proposed that HDACs additionally influence nuclear receptor signaling, the effect of class II HDACs on gender differences in cardiovascular disease remains unstudied

Massive IIA flux compactifications and U-dualities

We attempt to find a rigorous formulation for the massive type IIA orientifold compactifications of string theory introduced in hep-th/0505160. An approximate double T-duality converts this background into IIA string theory on a twisted torus, but various arguments indicate that the back reaction of the orientifold on this geometry is large. In particular, an AdS calculation of the entropy suggests a scaling appropriate for N M2-branes, in a certain limit of the compactification, though not the one studied in hep-th/0505160. The M-theory lift of this specific regime is not 4 dimensional. We suggest that the generic limit of the background corresponds to a situation analogous to F-theory, where the string coupling is small in some regions of a compact geometry, and large in others, so that neither a long wavelength 11D SUGRA expansion, nor a world sheet expansion exists for these compactifications. We end with a speculation on the nature of the generic compactification.Comment: JHEP3 LaTeX - 34 pages - 3 figures; v2: Added references; v3: mistake in entropy scaling corrected, major changes in conclusions; v4: changed claims about original DeWolfe et al. setup, JHEP versio

MADNESS: A Multiresolution, Adaptive Numerical Environment for Scientific Simulation

MADNESS (multiresolution adaptive numerical environment for scientific simulation) is a high-level software environment for solving integral and differential equations in many dimensions that uses adaptive and fast harmonic analysis methods with guaranteed precision based on multiresolution analysis and separated representations. Underpinning the numerical capabilities is a powerful petascale parallel programming environment that aims to increase both programmer productivity and code scalability. This paper describes the features and capabilities of MADNESS and briefly discusses some current applications in chemistry and several areas of physics

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Lived experiences of the diagnostic assessment process for Fetal Alcohol Spectrum Disorder: A systematic review of qualitative evidence

First published: 02 May 2023Early assessment and diagnosis of FASD are crucial in providing therapeutic interventions that aim to enhance meaningful participation and quality of life for individuals and their families, while reducing psychosocial difficulties that may arise during adolescence and adulthood. Individuals with lived experience of FASD have expertise based on their own lives and family needs. Their insights into the assessment and diagnostic process are valuable for improving service delivery and informing the provision of meaningful, person- and family-centered care. To date, reviews have focused broadly on the experiences of living with FASD. The aim of this systematic review is to synthesize qualitative evidence on the lived experiences of the diagnostic assessment process for FASD. Six electronic databases, including PubMed, the Cochrane Library, CINAH, EMBASE, PsycINFO, and Web of Science Core Collection were searched from inception until February 2021, and updated in December 2022. A manual search of reference lists of included studies identified additional studies for inclusion. The quality of included studies was assessed using the Critical Appraisal Skills Program Checklist for Qualitative Studies. Data from included studies were synthesized using a thematic analysis approach. GRADE-CERQual was used to assess confidence in the review findings. Ten studies met the selection criteria for inclusion in the review. Thematic analysis identified 10 first-level themes relating to four over-arching topics: (1) pre-assessment concerns and challenges, (2) the diagnostic assessment process, (3) receipt of the diagnosis, and (4) post-assessment adaptations and needs. GRADECERQual confidence ratings for each of the review themes were moderate to high. The findings from this review have implications for referral pathways, client-centered assessment processes, and post-diagnostic recommendations and support.Nicole Hayes, Kerryn Bagley, Nicole Hewlett, Elizabeth J. Elliott, Carmela F. Pestell, Matthew J. Gullo, Zachary Munn, Philippa Middleton, Prue Walker, Haydn Till, Dianne C. Shanley, Sophia L. Young, Nirosha Boaden, Delyse Hutchinson, Natalie R. Kippin, Amy Finlay- Jones, Rowena Friend, Doug Shelton, Alison Crichton, Natasha Rei

Applications and efficiencies of the first cat 63K DNA array

The development of high throughput SNP genotyping technologies has improved the genetic dissection of simple and complex traits in many species including cats. The properties of feline 62,897 SNPs Illumina Infinium iSelect DNA array are described using a dataset of over 2,000 feline samples, the most extensive to date, representing 41 cat breeds, a random bred population, and four wild felid species. Accuracy and efficiency of the array\u2019s genotypes and its utility in performing population-based analyses were evaluated. Average marker distance across the array was 37,741 Kb, and across the dataset, only 1% (625) of the markers exhibited poor genotyping and only 0.35% (221) showed Mendelian errors. Marker polymorphism varied across cat breeds and the average minor allele frequency (MAF) of all markers across domestic cats was 0.21. Population structure analysis confirmed a Western to Eastern structural continuum of cat breeds. Genome-wide linkage disequilibrium ranged from 50\u20131,500 Kb for domestic cats and 750 Kb for European wildcats (Felis silvestris silvestris). Array use in trait association mapping was investigated under different modes of inheritance, selection and population sizes. The efficient array design and cat genotype dataset continues to advance the understanding of cat breeds and will support monogenic health studies across feline breeds and populations

Identification of common genetic risk variants for autism spectrum disorder

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD.Peer reviewe