Photooxidation of 2-methyl-3-buten-2-ol (MBO) as a potential source of secondary organic aerosol

2-Methyl-3-buten-2-ol (MBO) is an important biogenic hydrocarbon emitted in large quantities by pine forests. Atmospheric photooxidation of MBO is known to lead to oxygenated compounds, such as glycolaldehyde, which is the precursor to glyoxal. Recent studies have shown that the reactive uptake of glyoxal onto aqueous particles can lead to formation of secondary organic aerosol (SOA). In this work, MBO photooxidation under high- and low-NO_x conditions was performed in dual laboratory chambers to quantify the yield of glyoxal and investigate the potential for SOA formation. The yields of glycolaldehyde and 2-hydroxy-2-methylpropanal (HMPR), fragmentation products of MBO photooxidation, were observed to be lower at lower NO_x concentrations. Overall, the glyoxal yield from MBO photooxidation was 25% under high-NO_x and 4% under low-NO_x conditions. In the presence of wet ammonium sulfate seed and under high-NO_x conditions, glyoxal uptake and SOA formation were not observed conclusively, due to relatively low (<30 ppb) glyoxal concentrations. Slight aerosol formation was observed under low-NO_x and dry conditions, with aerosol mass yields on the order of 0.1%. The small amount of SOA was not related to glyoxal uptake, but is likely a result of reactions similar to those that generate isoprene SOA under low-NO_x conditions. The difference in aerosol yields between MBO and isoprene photooxidation under low-NO_x conditions is consistent with the difference in vapor pressures between triols (from MBO) and tetrols (from isoprene). Despite its structural similarity to isoprene, photooxidation of MBO is not expected to make a significant contribution to SOA formation

Active Contour Texture Segmentation in Modulus Wavelet Feature Spaces

In this paper we discuss a model that is able to segment textures using active contours. Our technique is based on active contour techniques using. curve evolution. We build our model on properties of human vision, in that we segment the textures in a certain feature space. We will show the advantages of using modulus feature spaces. Wavelet coefficients are shown to exhibit local features both in space and frequency domains. We will implement our model in modulus wavelet subbands

The nature and role of trap states in a dendrimer-based organic field-effect transistor explosive sensor

We report the fabrication and charge transport characterization of carbazole dendrimer-based organic field-effect transistors (OFETs) for the sensing of explosive vapors. After exposure to para-nitrotoluene (pNT) vapor, the OFET channel carrier mobility decreases due to trapping induced by the absorbed pNT. The influence of trap states on transport in devices before and after exposure to pNT vapor has been determined using temperature-dependent measurements of the field-effect mobility. These data clearly show that the absorption of pNT vapor into the dendrimer active layer results in the formation of additional trap states. Such states inhibit charge transport by decreasing the density of conducting states. (C) 2013 AIP Publishing LLC

Modelling spatiotemporal patterns of dubas bug infestations on date palms in northern Oman: A geographical information system case study

The aim of this paper is to demonstrate how Geographical Information System (GIS) can be used effectively to study infestations of Dubas bug (DB), 'Ommatissus lybicus' Bergevin, in date palm ('Phoenix dactylifera L.') that occurred in northern Oman during 2006-2015. The ability to produce geographical and spatiotemporal layers using GIS is expected to serve an important role in both monitoring and surveillance of DB infestation and its impact in the study area. By using of spatial analytic and geostatistical functions in ArcGIS 10.3™, data that quantified the infestation levels of DB over a 10-year period from 2006 to 2015 were used to map and model the risk of infestation spatiotemporally. We modelled the spatiotemporal risk of DB infestation by performing hotspot analysis using the Getis-Ord statistic, Gi*. Our results show that annual hotspots over the study period were mainly concentrated in the mountain plains, particularly where farms are located between gradient elevations. Furthermore, the distribution pattern varied considerably with time and space. These results demonstrated the usefulness in following annual DB infestation patterns by studying the average seasonal infestation levels and distribution of hotspots as they can facilitate the allocation of resources for the treatment of infestations and allow for more effective monitoring of its influence on date palm trees

A Pilot Study Comparing HPV-Positive and HPV-Negative Head and Neck Squamous Cell Carcinomas by Whole Exome Sequencing.

Background. Next-generation sequencing of cancers has identified important therapeutic targets and biomarkers. The goal of this pilot study was to compare the genetic changes in a human papillomavirus- (HPV-)positive and an HPV-negative head and neck tumor. Methods. DNA was extracted from the blood and primary tumor of a patient with an HPV-positive tonsillar cancer and those of a patient with an HPV-negative oral tongue tumor. Exome enrichment was performed using the Agilent SureSelect All Exon Kit, followed by sequencing on the ABI SOLiD platform. Results. Exome sequencing revealed slightly more mutations in the HPV-negative tumor (73) in contrast to the HPV-positive tumor (58). Multiple mutations were noted in zinc finger genes (ZNF3, 10, 229, 470, 543, 616, 664, 638, 716, and 799) and mucin genes (MUC4, 6, 12, and 16). Mutations were noted in MUC12 in both tumors. Conclusions. HPV-positive HNSCC is distinct from HPV-negative disease in terms of evidence of viral infection, p16 status, and frequency of mutations. Next-generation sequencing has the potential to identify novel therapeutic targets and biomarkers in HNSCC

Functional health status in subjects after a motor vehicle accident, with emphasis on whiplash associated disorders: design of a descriptive, prospective inception cohort study

Contains fulltext : 70254.pdf (publisher's version ) (Open Access)BACKGROUND: The clinical consequences of whiplash injuries resulting from a motor vehicle accident (MVA) are poorly understood. Thereby, there is general lack of research on the development of disability in patients with acute and chronic Whiplash Associated Disorders. METHODS/DESIGN: The objective is to describe the design of an inception cohort study with a 1-year follow-up to determine risk factors for the development of symptoms after a low-impact motor vehicle accident, the prognosis of chronic disability, and costs. Victims of a low-impact motor vehicle accident will be eligible for participation. Participants with a Neck Disability Index (NDI) score of 7 or more will be classified as experiencing post-traumatic neck pain and will enter the experimental group. Participants without complaints (a NDI score less than 7) will enter the reference group. The cohort will be followed up by means of postal questionnaires and physical examinations at baseline, 3 months, 6 months, and 12 months. Recovery from whiplash-associated disorders will be measured in terms of perceived functional health, and employment status (return to work). Life tables will be generated to determine the 1-year prognosis of whiplash-associated disorders, and risk factors and prognostic factors will be assessed using multiple logistic regression analysis. DISCUSSION: Little is known about the development of symptoms and chronic disability after a whiplash injury. In the clinical setting, it is important to identify those people who are at risk of developing chronic symptoms.This inception prospective cohort study will provide insight in the influence of risk factors, of the development of functional health problems, and costs in people with whiplash-associated disorders

Direct Measurements of the Convective Recycling of the Upper Troposphere

We present a statistical representation of the aggregate effects of deep convection on the chemistry and dynamics of the Upper Troposphere (UT) based on direct aircraft observations of the chemical composition of the UT over the Eastern United States and Canada during summer. These measurements provide new and unique observational constraints on the chemistry occurring downwind of convection and the rate at which air in the UT is recycled, previously only the province of model analyses. These results provide quantitative measures that can be used to evaluate global climate and chemistry models

High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learning

Left ventricular hypertrophy (LVH) results from chronic remodeling caused by a broad range of systemic and cardiovascular disease including hypertension, aortic stenosis, hypertrophic cardiomyopathy, and cardiac amyloidosis. Early detection and characterization of LVH can significantly impact patient care but is limited by under-recognition of hypertrophy, measurement error and variability, and difficulty differentiating etiologies of LVH. To overcome this challenge, we present EchoNet-LVH - a deep learning workflow that automatically quantifies ventricular hypertrophy with precision equal to human experts and predicts etiology of LVH. Trained on 28,201 echocardiogram videos, our model accurately measures intraventricular wall thickness (mean absolute error [MAE] 1.4mm, 95% CI 1.2-1.5mm), left ventricular diameter (MAE 2.4mm, 95% CI 2.2-2.6mm), and posterior wall thickness (MAE 1.2mm, 95% CI 1.1-1.3mm) and classifies cardiac amyloidosis (area under the curve of 0.83) and hypertrophic cardiomyopathy (AUC 0.98) from other etiologies of LVH. In external datasets from independent domestic and international healthcare systems, EchoNet-LVH accurately quantified ventricular parameters (R2 of 0.96 and 0.90 respectively) and detected cardiac amyloidosis (AUC 0.79) and hypertrophic cardiomyopathy (AUC 0.89) on the domestic external validation site. Leveraging measurements across multiple heart beats, our model can more accurately identify subtle changes in LV geometry and its causal etiologies. Compared to human experts, EchoNet-LVH is fully automated, allowing for reproducible, precise measurements, and lays the foundation for precision diagnosis of cardiac hypertrophy. As a resource to promote further innovation, we also make publicly available a large dataset of 23,212 annotated echocardiogram videos