Finding Lips in Unconstrained Imagery for Improved Automatic Speech Recognition

Lip movement of a speaker conveys important visual speech information and can be exploited for Automatic Speech Recognition. While previous research demonstrated that visual modality is a viable tool for identifying speech, the visual information has yet to become utilized in mainstream ASR systems. One obstacle is the difficulty in building a robust visual front end that tracks lips accurately in a real-world condition. In this paper we present our current progress in addressing the issue. We examine the use of color information in detecting the lip region and report our results on the statistical analysis and modeling of lip hue images by examining hundreds of manually extracted lip images obtained from several databases. In addition to hue color, we also explore spatial and edge information derived from intensity and saturation images to improve the robustness of the lip detection. Successful application of this algorithm is demonstrated over imagery collected in visually challenging environments

Assessing the impact of future greenhouse gas emissions from natural gas production

Greenhouse gases (GHGs) produced by the extraction of natural gas are an important contributor to lifecycle emissions and account for a significant fraction of anthropogenic methane emissions in the USA. The timing as well as the magnitude of these emissions matters, as the short term climate warming impact of methane is up to 120 times that of CO 2 . This study uses estimates of CO 2 and methane emissions associated with different upstream operations to build a deterministic model of GHG emissions from conventional and unconventional gas fields as a function of time. By combining these emissions with a dynamic, techno-economic model of gas supply we assess their potential impact on the value of different types of project and identify stranded resources in various carbon price scenarios. We focus in particular on the effects of different emission metrics for methane, using the global warming potential (GWP) and the global temperature potential (GTP), with both fixed 20-year and 100-year CO 2 -equivalent values and in a time-dependent way based on a target year for climate stabilisation. We report a strong time dependence of emissions over the lifecycle of a typical field, and find that bringing forward the stabilisation year dramatically increases the importance of the methane contribution to these emissions. Using a commercial database of the remaining reserves of individual projects, we use our model to quantify future emissions resulting from the extraction of current US non-associated reserves. A carbon price of at least 400 USD/tonne CO 2 is effective in reducing cumulative GHGs by 30–60%, indicating that decarbonising the upstream component of the natural gas supply chain is achievable using carbon prices similar to those needed to decarbonise the energy system as a whole. Surprisingly, for large carbon prices, the choice of emission metric does not have a significant impact on cumulative emissions

Impact of drilling costs on the US gas industry: prospects for automation

Recent low gas prices have greatly increased pressure on drilling companies to reduce costs and increase efficiency. Field trials have shown that implementing automation can dramatically reduce drilling costs by reducing the time required to drill wells. This study uses the DYNamic upstreAm gAs MOdel (DYNAAMO), a new techno-economic, bottom-up model of natural gas supply, to quantitatively assess the economic impact of lower drilling costs on the US upstream gas industry. A sensitivity analysis of three key economic indicators is presented, with results quoted for the most common field types currently producing, including unconventional and offshore gas. While all operating environments show increased profitability from drilling automation, it is found that conventional onshore reserves can benefit to the greatest extent. For large gas fields, a 50% reduction in drilling costs is found to reduce initial project breakevens by up to 17 million USD per billion cubic metres (MUSD/BCM) and mid-plateau breakevens by up to 8 MUSD/BCM. In this same scenario, additional volumes of around 160 BCM of unconventional gas are shown to become commercial due to both the lower costs of additional production wells in mature fields and the viability of developing new resources held in smaller fields. The capital efficiency of onshore projects increases by 50%-100%, with initial project net present value (NPV) gains of up to 32%

Natural gas fuel and greenhouse gas emissions in trucks and ships

Natural gas is a transport fuel which may help reduce greenhouse gas emissions in shipping and trucks. However, there is some disagreement regarding the potential for natural gas to provide significant improvements relative to current ships and trucks. In 2015, road freight represented ~7% of global energy related CO2 emissions, with international shipping representing ~2.6% of global emissions. These emissions are also expected to grow, with some estimates suggesting road freight emission growing by a third, and shipping emissions growing by between 50% and 250% from 2012 to 2050, making absolute emissions reductions challenging. In addition, reducing emissions in ships and trucks has proved technically difficult given the relatively long distances that ships and trucks travel. This paper documents a systematic review of literature detailing well-to-wheel/wake greenhouse gas emissions and economic costs in moving from diesel and heavy fuel oil to natural gas as a fuel for trucks and ships. The review found a number of important issues for greenhouse gas reduction. First, moderate greenhouse gas reductions of 10% were found when switching to natural gas from heavy fuel oil in shipping when comparing the lowest estimates. Comparing lowest well-to-wheel greenhouse gas emissions estimates for trucks, the benefit of switching to natural gas fuel is approximately a 16% reduction in greenhouse gas emissions. However, these emissions are highly variable, driven particularly by methane emissions in exhaust gas. Given this, in the worst cases natural gas ships and trucks emit more greenhouse gasses than the diesel trucks and heavy fuel oil ships that they would replace. It appears relatively cost effective to switch to natural gas as a transport fuel in ships and trucks. However, the limited emissions reduction potential raises questions for the ongoing role of natural gas to reduce greenhouse gas emissions in line with the challenging greenhouse gas reduction targets emerging in the transport sector

Putative psychosis genes in the prefrontal cortex: combined analysis of gene expression microarrays

<p>Abstract</p> <p>Background</p> <p>Recent studies have shown similarities between schizophrenia and bipolar disorder in phenotypes and in genotypes, and those studies have contributed to an ongoing re-evaluation of the traditional dichotomy between schizophrenia and bipolar disorder. Bipolar disorder with psychotic features may be closely related to schizophrenia and therefore, psychosis may be an alternative phenotype compared to the traditional diagnosis categories.</p> <p>Methods</p> <p>We performed a cross-study analysis of 7 gene expression microarrays that include both psychosis and non-psychosis subjects. These studies include over 400 microarray samples (163 individual subjects) on 3 different Affymetrix microarray platforms.</p> <p>Results</p> <p>We found that 110 transcripts are differentially regulated (p < 0.001) in psychosis after adjusting for confounding variables with a multiple regression model. Using a quantitative PCR, we validated a set of genes such as up-regulated metallothioneins (MT1E, MT1F, MT1H, MT1K, MT1X, MT2A and MT3) and down-regulated neuropeptides (SST, TAC1 and NPY) in the dorsolateral prefrontal cortex of psychosis patients.</p> <p>Conclusion</p> <p>This study demonstrates the advantages of cross-study analysis in detecting consensus changes in gene expression across multiple microarray studies. Differential gene expression between individuals with and without psychosis suggests that psychosis may be a useful phenotypic variable to complement the traditional diagnosis categories.</p

Manganese Superoxide Dismutase: Guardian of the Powerhouse

The mitochondrion is vital for many metabolic pathways in the cell, contributing all or important constituent enzymes for diverse functions such as β-oxidation of fatty acids, the urea cycle, the citric acid cycle, and ATP synthesis. The mitochondrion is also a major site of reactive oxygen species (ROS) production in the cell. Aberrant production of mitochondrial ROS can have dramatic effects on cellular function, in part, due to oxidative modification of key metabolic proteins localized in the mitochondrion. The cell is equipped with myriad antioxidant enzyme systems to combat deleterious ROS production in mitochondria, with the mitochondrial antioxidant enzyme manganese superoxide dismutase (MnSOD) acting as the chief ROS scavenging enzyme in the cell. Factors that affect the expression and/or the activity of MnSOD, resulting in diminished antioxidant capacity of the cell, can have extraordinary consequences on the overall health of the cell by altering mitochondrial metabolic function, leading to the development and progression of numerous diseases. A better understanding of the mechanisms by which MnSOD protects cells from the harmful effects of overproduction of ROS, in particular, the effects of ROS on mitochondrial metabolic enzymes, may contribute to the development of novel treatments for various diseases in which ROS are an important component

Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyper-activity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.Peer reviewe

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