Reducing bias in auditory duration reproduction by integrating the reproduced signal

Duration estimation is known to be far from veridical and to differ for sensory estimates and motor reproduction. To investigate how these differential estimates are integrated for estimating or reproducing a duration and to examine sensorimotor biases in duration comparison and reproduction tasks, we compared estimation biases and variances among three different duration estimation tasks: perceptual comparison, motor reproduction, and auditory reproduction (i.e. a combined perceptual-motor task). We found consistent overestimation in both motor and perceptual-motor auditory reproduction tasks, and the least overestimation in the comparison task. More interestingly, compared to pure motor reproduction, the overestimation bias was reduced in the auditory reproduction task, due to the additional reproduced auditory signal. We further manipulated the signal-to-noise ratio (SNR) in the feedback/comparison tones to examine the changes in estimation biases and variances. Considering perceptual and motor biases as two independent components, we applied the reliability-based model, which successfully predicted the biases in auditory reproduction. Our findings thus provide behavioral evidence of how the brain combines motor and perceptual information together to reduce duration estimation biases and improve estimation reliability

Lack of significant association of an insertion/deletion polymorphism in the angiotensin converting enzyme (ACE) gene with tropical calcific pancreatitis

BACKGROUND: The genetic basis of tropical calcific pancreatitis (TCP) is different and is explained by mutations in the pancreatic secretory trypsin inhibitor (SPINK1) gene. However, mutated SPINK1 does not account for the disease in all the patients, neither does it explain the phenotypic heterogeneity between TCP and fibro-calculous pancreatic diabetes (FCPD). Recent studies suggest a crucial role for pancreatic renin-angiotensin system during chronic hypoxia in acute pancreatitis and for angiotensin converting enzyme (ACE) inhibitors in reducing pancreatic fibrosis in experimental models. We investigated the association of ACE gene insertion/deletion (I/D) polymorphism in TCP patients using a case-control approach. Since SPINK1 mutations are proposed a modifier role, we also investigated its interaction with the ACE gene variant. METHODS: We analyzed the I/D polymorphism in the ACE gene (g.11417_11704del287) in 171 subjects comprising 91 TCP and 80 FCPD patients and compared the allelic and genotypic frequency in them with 99 healthy ethnically matched control subjects. RESULTS: We found 46% and 21% of TCP patients, 56% and 19.6% of FCPD patients and 54.5% and 19.2% of the healthy controls carrying the I/D and D/D genotypes respectively (P>0.05). No significant difference in the clinical picture was observed between patients with and without the del allele at the ACE in/del polymorphism in both categories. No association was observed with the presence or absence of N34S SPINK1 mutation in these patients. CONCLUSION: We conclude that the ACE insertion/deletion variant does not show any significant association with the pathogenesis, fibrosis and progression of tropical calcific pancreatitis and the fibro-calculous pancreatic diabetes

Status and Prospects of ZnO-Based Resistive Switching Memory Devices

In the advancement of the semiconductor device technology, ZnO could be a prospective alternative than the other metal oxides for its versatility and huge applications in different aspects. In this review, a thorough overview on ZnO for the application of resistive switching memory (RRAM) devices has been conducted. Various efforts that have been made to investigate and modulate the switching characteristics of ZnO-based switching memory devices are discussed. The use of ZnO layer in different structure, the different types of filament formation, and the different types of switching including complementary switching are reported. By considering the huge interest of transparent devices, this review gives the concrete overview of the present status and prospects of transparent RRAM devices based on ZnO. ZnO-based RRAM can be used for flexible memory devices, which is also covered here. Another challenge in ZnO-based RRAM is that the realization of ultra-thin and low power devices. Nevertheless, ZnO not only offers decent memory properties but also has a unique potential to be used as multifunctional nonvolatile memory devices. The impact of electrode materials, metal doping, stack structures, transparency, and flexibility on resistive switching properties and switching parameters of ZnO-based resistive switching memory devices are briefly compared. This review also covers the different nanostructured-based emerging resistive switching memory devices for low power scalable devices. It may give a valuable insight on developing ZnO-based RRAM and also should encourage researchers to overcome the challenges

Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)

Applying polygenic risk scoring for psychiatric disorders to a large family with bipolar disorder and major depressive disorder

Psychiatric disorders are thought to have a complex genetic pathology consisting of interplay of common and rare variation. Traditionally, pedigrees are used to shed light on the latter only, while here we discuss the application of polygenic risk scores to also highlight patterns of common genetic risk. We analyze polygenic risk scores for psychiatric disorders in a large pedigree (n similar to 260) in which 30% of family members suffer from major depressive disorder or bipolar disorder. Studying patterns of assortative mating and anticipation, it appears increased polygenic risk is contributed by affected individuals who married into the family, resulting in an increasing genetic risk over generations. This may explain the observation of anticipation in mood disorders, whereby onset is earlier and the severity increases over the generations of a family. Joint analyses of rare and common variation may be a powerful way to understand the familial genetics of psychiatric disorders

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

The genetics of the mood disorder spectrum:genome-wide association analyses of over 185,000 cases and 439,000 controls

Background Mood disorders (including major depressive disorder and bipolar disorder) affect 10-20% of the population. They range from brief, mild episodes to severe, incapacitating conditions that markedly impact lives. Despite their diagnostic distinction, multiple approaches have shown considerable sharing of risk factors across the mood disorders. Methods To clarify their shared molecular genetic basis, and to highlight disorder-specific associations, we meta-analysed data from the latest Psychiatric Genomics Consortium (PGC) genome-wide association studies of major depression (including data from 23andMe) and bipolar disorder, and an additional major depressive disorder cohort from UK Biobank (total: 185,285 cases, 439,741 controls; non-overlapping N = 609,424). Results Seventy-three loci reached genome-wide significance in the meta-analysis, including 15 that are novel for mood disorders. More genome-wide significant loci from the PGC analysis of major depression than bipolar disorder reached genome-wide significance. Genetic correlations revealed that type 2 bipolar disorder correlates strongly with recurrent and single episode major depressive disorder. Systems biology analyses highlight both similarities and differences between the mood disorders, particularly in the mouse brain cell-types implicated by the expression patterns of associated genes. The mood disorders also differ in their genetic correlation with educational attainment – positive in bipolar disorder but negative in major depressive disorder. Conclusions The mood disorders share several genetic associations, and can be combined effectively to increase variant discovery. However, we demonstrate several differences between these disorders. Analysing subtypes of major depressive disorder and bipolar disorder provides evidence for a genetic mood disorders spectrum

Genetic Overlap Between Alzheimer’s Disease and Bipolar Disorder Implicates the MARK2 and VAC14 Genes

Background: Alzheimer's disease (AD) and bipolar disorder (BIP) are complex traits influenced by numerous common genetic variants, most of which remain to be detected. Clinical and epidemiological evidence suggest that AD and BIP are related. However, it is not established if this relation is of genetic origin. Here, we applied statistical methods based on the conditional false discovery rate (FDR) framework to detect genetic overlap between AD and BIP and utilized this overlap to increase the power to identify common genetic variants associated with either or both traits. Methods: We obtained genome wide association studies data from the International Genomics of Alzheimer's Project part 1 (17,008 AD cases and 37,154 controls) and the Psychiatric Genetic Consortium Bipolar Disorder Working Group (20,352 BIP cases and 31,358 controls). We used conditional QQ-plots to assess overlap in common genetic variants between AD and BIP. We exploited the genetic overlap to re-rank test-statistics for AD and BIP and improve detection of genetic variants using the conditional FDR framework. Results: Conditional QQ-plots demonstrated a polygenic overlap between AD and BIP. Using conditional FDR, we identified one novel genomic locus associated with AD, and nine novel loci associated with BIP. Further, we identified two novel loci jointly associated with AD and BIP implicating the MARK2 gene (lead SNP rs10792421, conjunctional FDR=0.030, same direction of effect) and the VAC14 gene (lead SNP rs11649476, conjunctional FDR=0.022, opposite direction of effect). Conclusions: We found polygenic overlap between AD and BIP and identified novel loci for each trait and two jointly associated loci. Further studies should examine if the shared loci implicating the MARK2 and VAC14 genes could explain parts of the shared and distinct features of AD and BIP