Robust Linear Models for Cis-eQTL Analysis

Expression Quantitative Trait Loci (eQTL) analysis enables characterisation of functional genetic variation influencing expression levels of individual genes. In outbread populations, including humans, eQTLs are commonly analysed using the conventional linear model, adjusting for relevant covariates, assuming an allelic dosage model and a Gaussian error term. However, gene expression data generally have noise that induces heavy-tailed errors relative to the Gaussian distribution and often include atypical observations, or outliers. Such departures from modelling assumptions can lead to an increased rate of type II errors (false negatives), and to some extent also type I errors (false positives). Careful model checking can reduce the risk of type-I errors but often not type II errors, since it is generally too time-consuming to carefully check all models with a non-significant effect in large-scale and genome-wide studies. Here we propose the application of a robust linear model for eQTL analysis to reduce adverse effects of deviations from the assumption of Gaussian residuals. We present results from a simulation study as well as results from the analysis of real eQTL data sets. Our findings suggest that in many situations robust models have the potential to provide more reliable eQTL results compared to conventional linear models, particularly in respect to reducing type II errors due to non-Gaussian noise. Post-genomic data, such as that generated in genome-wide eQTL studies, are often noisy and frequently contain atypical observations. Robust statistical models have the potential to provide more reliable results and increased statistical power under non-Gaussian conditions. The results presented here suggest that robust models should be considered routinely alongside other commonly used methodologies for eQTL analysis.NonePublishe

Emergent scale-free networks

Many complex systems--from social and communication networks to biological networks and the Internet--are thought to exhibit scale-free structure. However, prevailing explanations rely on the constant addition of new nodes, an assumption that fails dramatically in some real-world settings. Here, we propose a model in which nodes are allowed to die, and their connections rearrange under a mixture of preferential and random attachment. With these simple dynamics, we show that networks self-organize towards scale-free structure, with a power-law exponent $\gamma = 1 + \frac{1}{p}$ that depends only on the proportion $p$ of preferential (rather than random) attachment. Applying our model to several real networks, we infer $p$ directly from data, and predict the relationship between network size and degree heterogeneity. Together, these results establish that realistic scale-free structure can emerge naturally in networks of constant size and density, with broad implications for the structure and function of complex systems.Comment: 24 pages, 5 figure

Attenuation of Ultrasonic Waves Generated from Laser Ultrasound during Annealing of Steel; a Comparison between Theory and Experiment and Potential Application to Additive Manufacturing

The advancement of additive manufacturing methods for the production of metallic parts has initiated the potential development of materials with tailored microstructures to enhance their material properties. To help facilitate the development, methods based on ultrasonic grain scattering are proposed to provide in-situ monitoring of the microstructure’s evolution during the build process. In this work, the longitudinal attenuation coefficient is considered, theoretically and experimentally, as a function of temperature during an annealing process of steel. Theoretically, an iterative solution to the attenuation model of Stanke and Kino is given. The theory is compared against experimental measurements of the longitudinal attenuation coefficient for a steel sample taken at various stages of annealing. Laser ultrasound was employed because it is a remote technique that minimizes unwanted temperature related effects. The annealing process brought the sample from room temperature to 950 o C. A phase transformation from ferrite to austenite occurred at 800 o C, which caused a significant drop in the measured attenuation coefficient. The theoretical attenuation model borrowed previously measured temperature-dependent single-crystal elastic constants of pure iron as model inputs. A mixing formula that considers the volume fraction of ferrite to austenite was applied near the 800 o C mark where the drop in attenuation appeared. Remarkably, the theoretical attenuation model almost exactly reproduced the experimental data points. This concurrence supports: (1) the employment of laser ultrasound for measurement of the attenuation during heating of materials, (2) the suitability of theoretical ultrasonic grain scattering models during highly transient temperature behavior, and (3) the ability of the theoretical attenuation model to represent the effect of a phase transformation

Social Presence in Online Counselor Education

Outcome research in online counselor education is lacking as is the focus on online teaching andragogy. To address this gap, the Community of Inquiry framework and social presence are discussed within the context of online learning in a counselor education program. Data were collected in a counselor education program in the mid-Atlantic comparing online and on-campus learning outcomes and perceptions of social presence in the classroom. On-campus learners had significantly higher perceptions of social presence when compared with online learners, although perceived level of social presence was not correlated with learning outcomes. Implications for counselor education are discussed

Emergent scale-free networks

Many complex systems—from the Internet to social, biological, and communication networks—are thought to exhibit scale-free structure. However, prevailing explanations require that networks grow over time, an assumption that fails in some real-world settings. Here, we explain how scale-free structure can emerge without growth through network self-organization. Beginning with an arbitrary network, we allow connections to detach from random nodes and then reconnect under a mixture of preferential and random attachment. While the numbers of nodes and edges remain fixed, the degree distribution evolves toward a power-law with an exponent $y = 1 + \frac{1}{p}$ that depends only on the proportion p of preferential (rather than random) attachment. Applying our model to several real networks, we infer p directly from data and predict the relationship between network size and degree heterogeneity. Together, these results establish how scale-free structure can arise in networks of constant size and density, with broad implications for the structure and function of complex systems

miR-34a-/- mice are susceptible to diet-induced obesity

Objective: MicroRNA (miR)−34a regulates inflammatory pathways, and increased transcripts have been observed in serum and subcutaneous adipose of subjects who have obesity and type 2 diabetes. Therefore, the role of miR-34a in adipose tissue inflammation and lipid metabolism in murine diet-induced obesity was investigated. Methods: Wild-type (WT) and miR-34a−/− mice were fed chow or high-fat diet (HFD) for 24 weeks. WT and miR-34a−/− bone marrow-derived macrophages were cultured in vitro with macrophage colony-stimulating factor (M-CSF). Brown and white preadipocytes were cultured from the stromal vascular fraction (SVF) of intrascapular brown and epididymal white adipose tissue (eWAT), with rosiglitazone. Results: HFD-fed miR-34a−/− mice were significantly heavier with a greater increase in eWAT weight than WT. miR-34a−/− eWAT had a smaller adipocyte area, which significantly increased with HFD. miR-34a−/− eWAT showed basal increases in Cd36, Hmgcr, Lxrα, Pgc1α, and Fasn. miR-34a−/− intrascapular brown adipose tissue had basal reductions in c/ebpα and c/ebpβ, with in vitro miR-34a−/− white adipocytes showing increased lipid content. An F4/80high macrophage population was present in HFD miR-34a−/− eWAT, with increased IL-10 transcripts and serum IL-5 protein. Finally, miR-34a−/− bone marrow-derived macrophages showed an ablated CXCL1 response to tumor necrosis factor-α. Conclusions: These findings suggest a multifactorial role of miR-34a in controlling susceptibility to obesity, by regulating inflammatory and metabolic pathways

Stroke penumbra defined by an MRI-based oxygen challenge technique: 2. Validation based on the consequences of reperfusion

Magnetic resonance imaging (MRI) with oxygen challenge (T2* OC) uses oxygen as a metabolic biotracer to define penumbral tissue based on CMRO2 and oxygen extraction fraction. Penumbra displays a greater T2* signal change during OC than surrounding tissue. Since timely restoration of cerebral blood flow (CBF) should salvage penumbra, T2* OC was tested by examining the consequences of reperfusion on T2* OC-defined penumbra. Transient ischemia (109±20 minutes) was induced in male Sprague-Dawley rats (n=8). Penumbra was identified on T2*-weighted MRI during OC. Ischemia and ischemic injury were identified on CBF and apparent diffusion coefficient maps, respectively. Reperfusion was induced and scans repeated. T2 for final infarct and T2* OC were run on day 7. T2* signal increase to OC was 3.4% in contralateral cortex and caudate nucleus and was unaffected by reperfusion. In OC-defined penumbra, T2* signal increased by 8.4%±4.1% during ischemia and returned to 3.25%±0.8% following reperfusion. Ischemic core T2* signal increase was 0.39%±0.47% during ischemia and 0.84%±1.8% on reperfusion. Penumbral CBF increased from 41.94±13 to 116.5±25 mL per 100 g per minute on reperfusion. On day 7, OC-defined penumbra gave a normal OC response and was located outside the infarct. T2* OC-defined penumbra recovered when CBF was restored, providing further validation of the utility of T2* OC for acute stroke management

Stroke penumbra defined by an MRI-based oxygen challenge technique: 1. validation using [14C]2-deoxyglucose autoradiography

Accurate identification of ischemic penumbra will improve stroke patient selection for reperfusion therapies and clinical trials. Current magnetic resonance imaging (MRI) techniques have limitations and lack validation. Oxygen challenge T2* MRI (T2* OC) uses oxygen as a biotracer to detect tissue metabolism, with penumbra displaying the greatest T2* signal change during OC. [14C]2-deoxyglucose (2-DG) autoradiography was combined with T2* OC to determine metabolic status of T2*-defined penumbra. Permanent middle cerebral artery occlusion was induced in anesthetized male Sprague-Dawley rats (n=6). Ischemic injury and perfusion deficit were determined by diffusion- and perfusion-weighted imaging, respectively. At 147±32 minutes after stroke, T2* signal change was measured during a 5-minute 100% OC, immediately followed by 125 μCi/kg 2-DG, intravenously. Magnetic resonance images were coregistered with the corresponding autoradiograms. Regions of interest were located within ischemic core, T2*-defined penumbra, equivalent contralateral structures, and a region of hyperglycolysis. A T2* signal increase of 9.22%±3.9% (mean±s.d.) was recorded in presumed penumbra, which displayed local cerebral glucose utilization values equivalent to contralateral cortex. T2* signal change was negligible in ischemic core, 3.2%±0.78% in contralateral regions, and 1.41%±0.62% in hyperglycolytic tissue, located outside OC-defined penumbra and within the diffusion abnormality. The results support the utility of OC-MRI to detect viable penumbral tissue follow

Potential use of oxygen as a metabolic biosensor in combination with T2*-weighted MRI to define the ischemic penumbra

We describe a novel magnetic resonance imaging technique for detecting metabolism indirectly through changes in oxyhemoglobin:deoxyhemoglobin ratios and T2* signal change during ‘oxygen challenge’ (OC, 5 mins 100% O2). During OC, T2* increase reflects O2 binding to deoxyhemoglobin, which is formed when metabolizing tissues take up oxygen. Here OC has been applied to identify tissue metabolism within the ischemic brain. Permanent middle cerebral artery occlusion was induced in rats. In series 1 scanning (n=5), diffusion-weighted imaging (DWI) was performed, followed by echo-planar T2* acquired during OC and perfusion-weighted imaging (PWI, arterial spin labeling). Oxygen challenge induced a T2* signal increase of 1.8%, 3.7%, and 0.24% in the contralateral cortex, ipsilateral cortex within the PWI/DWI mismatch zone, and ischemic core, respectively. T2* and apparent diffusion coefficient (ADC) map coregistration revealed that the T2* signal increase extended into the ADC lesion (3.4%). In series 2 (n=5), FLASH T2* and ADC maps coregistered with histology revealed a T2* signal increase of 4.9% in the histologically defined border zone (55% normal neuronal morphology, located within the ADC lesion boundary) compared with a 0.7% increase in the cortical ischemic core (92% neuronal ischemic cell change, core ADC lesion). Oxygen challenge has potential clinical utility and, by distinguishing metabolically active and inactive tissues within hypoperfused regions, could provide a more precise assessment of penumbra