Modeling Camera Effects to Improve Visual Learning from Synthetic Data

Recent work has focused on generating synthetic imagery to increase the size and variability of training data for learning visual tasks in urban scenes. This includes increasing the occurrence of occlusions or varying environmental and weather effects. However, few have addressed modeling variation in the sensor domain. Sensor effects can degrade real images, limiting generalizability of network performance on visual tasks trained on synthetic data and tested in real environments. This paper proposes an efficient, automatic, physically-based augmentation pipeline to vary sensor effects --chromatic aberration, blur, exposure, noise, and color cast-- for synthetic imagery. In particular, this paper illustrates that augmenting synthetic training datasets with the proposed pipeline reduces the domain gap between synthetic and real domains for the task of object detection in urban driving scenes

L-Arginine promotes gut hormone release and reduces food intake in rodents

Aims: To investigate the anorectic effect of L‐arginine (L‐Arg) in rodents. Methods: We investigated the effects of L‐Arg on food intake, and the role of the anorectic gut hormones glucagon‐like peptide‐1 (GLP‐1) and peptide YY (PYY), the G‐protein‐coupled receptor family C group 6 member A (GPRC6A) and the vagus nerve in mediating these effects in rodents. Results: Oral gavage of L‐Arg reduced food intake in rodents, and chronically reduced cumulative food intake in diet‐induced obese mice. Lack of the GPRC6A in mice and subdiaphragmatic vagal deafferentation in rats did not influence these anorectic effects. L‐Arg stimulated GLP‐1 and PYY release in vitro and in vivo. Pharmacological blockade of GLP‐1 and PYY receptors did not influence the anorectic effect of L‐Arg. L‐Arg‐mediated PYY release modulated net ion transport across the gut mucosa. Intracerebroventricular (i.c.v.) and intraperitoneal (i.p.) administration of L‐Arg suppressed food intake in rats. Conclusions: L‐Arg reduced food intake and stimulated gut hormone release in rodents. The anorectic effect of L‐Arg is unlikely to be mediated by GLP‐1 and PYY, does not require GPRC6A signalling and is not mediated via the vagus. I.c.v. and i.p. administration of L‐Arg suppressed food intake in rats, suggesting that L‐Arg may act on the brain to influence food intake. Further work is required to determine the mechanisms by which L‐Arg suppresses food intake and its utility in the treatment of obesity

Estimating the Total Number of Susceptibility Variants Underlying Complex Diseases from Genome-Wide Association Studies

Recently genome-wide association studies (GWAS) have identified numerous susceptibility variants for complex diseases. In this study we proposed several approaches to estimate the total number of variants underlying these diseases. We assume that the variance explained by genetic markers (Vg) follow an exponential distribution, which is justified by previous studies on theories of adaptation. Our aim is to fit the observed distribution of Vg from GWAS to its theoretical distribution. The number of variants is obtained by the heritability divided by the estimated mean of the exponential distribution. In practice, due to limited sample sizes, there is insufficient power to detect variants with small effects. Therefore the power was taken into account in fitting. Besides considering the most significant variants, we also tried to relax the significance threshold, allowing more markers to be fitted. The effects of false positive variants were removed by considering the local false discovery rates. In addition, we developed an alternative approach by directly fitting the z-statistics from GWAS to its theoretical distribution. In all cases, the “winner's curse” effect was corrected analytically. Confidence intervals were also derived. Simulations were performed to compare and verify the performance of different estimators (which incorporates various means of winner's curse correction) and the coverage of the proposed analytic confidence intervals. Our methodology only requires summary statistics and is able to handle both binary and continuous traits. Finally we applied the methods to a few real disease examples (lipid traits, type 2 diabetes and Crohn's disease) and estimated that hundreds to nearly a thousand variants underlie these traits

A comprehensive survey of genomic alterations in gastric cancer reveals systematic patterns of molecular exclusivity and co-occurrence among distinct therapeutic targets

Objective: Gastric cancer is a major gastrointestinal malignancy for which targeted therapies are emerging as treatment options. This study sought to identify the most prevalent molecular targets in gastric cancer and to elucidate systematic patterns of exclusivity and co-occurrence among these targets, through comprehensive genomic analysis of a large panel of gastric cancers. Design: Using high-resolution single nucleotide polymorphism arrays, copy number alterations were profiled in a panel of 233 gastric cancers (193 primary tumours, 40 cell lines) and 98 primary matched gastric non-malignant samples. For selected alterations, their impact on gene expression and clinical outcome were evaluated. Results: 22 recurrent focal alterations (13 amplifications and nine deletions) were identified. These included both known targets (FGFR2, ERBB2) and also novel genes in gastric cancer (KLF5, GATA6). Receptor tyrosine kinase (RTK)/RAS alterations were found to be frequent in gastric cancer. This study also demonstrates, for the first time, that these alterations occur in a mutually exclusive fashion, with KRAS gene amplifications highlighting a clinically relevant but previously underappreciated gastric cancer subgroup. FGFR2-amplified gastric cancers were also shown to be sensitive to dovitinib, an orally bioavailable FGFR/VEGFR targeting agent, potentially representing a subtype-specific therapy for FGFR2-amplified gastric cancers. Conclusion: The study demonstrates the existence of five distinct gastric cancer patient subgroups, defined by the signature genomic alterations FGFR2 (9% of tumours), KRAS (9%), EGFR (8%), ERBB2 (7%) and MET (4%). Collectively, these subgroups suggest that at least 37% of gastric cancer patients may be potentially treatable by RTK/RAS directed therapies

The thalamic mGluR1-PLC??4 pathway is critical in sleep architecture

The transition from wakefulness to a nonrapid eye movement (NREM) sleep state at the onset of sleep involves a transition from low-voltage, high-frequency irregular electroencephalography (EEG) waveforms to large-amplitude, low-frequency EEG waveforms accompanying synchronized oscillatory activity in the thalamocortical circuit. The thalamocortical circuit consists of reciprocal connections between the thalamus and cortex. The cortex sends strong excitatory feedback to the thalamus, however the function of which is unclear. In this study, we investigated the role of the thalamic metabotropic glutamate receptor 1 (mGluR1)-phospholipase C ??4 (PLC??4) pathway in sleep control in PLC??4-deficient (PLC??4-/-) mice. The thalamic mGluR1-PLC??4 pathway contains synapses that receive corticothalamic inputs. In PLC??4-/- mice, the transition from wakefulness to the NREM sleep state was stimulated, and the NREM sleep state was stabilized, which resulted in increased NREM sleep. The power density of delta (??) waves increased in parallel with the increased NREM sleep. These sleep phenotypes in PLC??4-/- mice were consistent in TC-restricted PLC??4 knockdown mice. Moreover, in vitro intrathalamic oscillations were greatly enhanced in the PLC??4-/- slices. The results of our study showed that thalamic mGluR1-PLC??4 pathway was critical in controlling sleep architecture.ope

Accounting Problems Under the Excess Profits Tax

DNA vaccines based on subunits from pathogens have several advantages over other vaccine strategies. DNA vaccines can easily be modified, they show good safety profiles, are stable and inexpensive to produce, and the immune response can be focused to the antigen of interest. However, the immunogenicity of DNA vaccines which is generally quite low needs to be improved. Electroporation and co-delivery of genetically encoded immune adjuvants are two strategies aiming at increasing the efficacy of DNA vaccines. Here, we have examined whether targeting to antigen-presenting cells (APC) could increase the immune response to surface envelope glycoprotein (Env) gp120 from Human Immunodeficiency Virus type 1 (HIV- 1). To target APC, we utilized a homodimeric vaccine format denoted vaccibody, which enables covalent fusion of gp120 to molecules that can target APC. Two molecules were tested for their efficiency as targeting units: the antibody-derived single chain Fragment variable (scFv) specific for the major histocompatilibility complex (MHC) class II I-E molecules, and the CC chemokine ligand 3 (CCL3). The vaccines were delivered as DNA into muscle of mice with or without electroporation. Targeting of gp120 to MHC class II molecules induced antibodies that neutralized HIV-1 and that persisted for more than a year after one single immunization with electroporation. Targeting by CCL3 significantly increased the number of HIV-1 gp120-reactive CD8(+) T cells compared to non-targeted vaccines and gp120 delivered alone in the absence of electroporation. The data suggest that chemokines are promising molecular adjuvants because small amounts can attract immune cells and promote immune responses without advanced equipment such as electroporation.Funding Agencies|Research Council of Norway; Odd Fellow</p

A Unifying Framework for Evaluating the Predictive Power of Genetic Variants Based on the Level of Heritability Explained

An increasing number of genetic variants have been identified for many complex diseases. However, it is controversial whether risk prediction based on genomic profiles will be useful clinically. Appropriate statistical measures to evaluate the performance of genetic risk prediction models are required. Previous studies have mainly focused on the use of the area under the receiver operating characteristic (ROC) curve, or AUC, to judge the predictive value of genetic tests. However, AUC has its limitations and should be complemented by other measures. In this study, we develop a novel unifying statistical framework that connects a large variety of predictive indices together. We showed that, given the overall disease probability and the level of variance in total liability (or heritability) explained by the genetic variants, we can estimate analytically a large variety of prediction metrics, for example the AUC, the mean risk difference between cases and non-cases, the net reclassification improvement (ability to reclassify people into high- and low-risk categories), the proportion of cases explained by a specific percentile of population at the highest risk, the variance of predicted risks, and the risk at any percentile. We also demonstrate how to construct graphs to visualize the performance of risk models, such as the ROC curve, the density of risks, and the predictiveness curve (disease risk plotted against risk percentile). The results from simulations match very well with our theoretical estimates. Finally we apply the methodology to nine complex diseases, evaluating the predictive power of genetic tests based on known susceptibility variants for each trait

Robust Association Tests Under Different Genetic Models, Allowing for Binary or Quantitative Traits and Covariates

The association of genetic variants with outcomes is usually assessed under an additive model, for example by the trend test. However, misspecification of the genetic model will lead to a reduction in power. More robust tests for association might therefore be preferred. A useful approach is to consider the maximum of the three test statistics under additive, dominant and recessive models (MAX3). The p-value however has to be adjusted to maintain the type I error rate. Previous studies and software on robust association tests have focused on binary traits without covariates. In this study we developed an analytic approach to robust association tests using MAX3, allowing for quantitative or binary traits as well as covariates. The p-values from our theoretical calculations match very well with those from a bootstrap resampling procedure. The methodology is implemented in the R package RobustSNP which is able to handle both small-scale studies and GWAS. The package and documentation are available at http://sites.google.com/site/honcheongso/software/robustsnp

Infant head growth in male siblings of children with and without autism spectrum disorders

Previous research has indicated that children with autism exhibit accelerated head growth (HG) in infancy, although the timing of acceleration varies between studies. We examined infant HG trajectory as a candidate autism endophenotype by studying sibling pairs. We retrospectively obtained serial head orbitofrontal circumference measurements of: a) 48 sibling pairs in which one (n = 28) or both (n = 20) sibs were affected by an autism spectrum disorder (ASD); and b) 85 control male sibling pairs. Rate of HG of ASD subjects was slightly accelerated compared to controls, but the magnitude of difference was below the limit of reliability of standard measurement methods. Sibling intra class correlation for rate of HG was highly statistically significant; the magnitude was significantly stronger among autism-affected families (ICC = .63) than among controls (ICC = .26), p < .01. Infant HG trajectory appears familial—possibly endophenotypic—but was not a reliable marker of autism risk among siblings of ASD probands in this sample