The Quantum Cosmological Wavefunction at Very Early Times for a Quadratic Gravity Theory

The quantum cosmological wavefunction for a quadratic gravity theory derived from the heterotic string effective action is obtained near the inflationary epoch and during the initial Planck era. Neglecting derivatives with respect to the scalar field, the wavefunction would satisfy a third-order differential equation near the inflationary epoch which has a solution that is singular in the scale factor limit $a(t)\to 0$. When scalar field derivatives are included, a sixth-order differential equation is obtained for the wavefunction and the solution by Mellin transform is regular in the $a\to 0$ limit. It follows that inclusion of the scalar field in the quadratic gravity action is necessary for consistency of the quantum cosmology of the theory at very early times.Comment: Tex, 13 page

Classification of pathology in diabetic eye disease

Proliferative diabetic retinopathy is a complication of diabetes that can eventually lead to blindness. Early identification of this complication reduces the risk of blindness by initiating timely treatment. We report the utility of pattern analysis tools linked with a simple linear discriminant analysis that not only identifies new vessel growth in the retinal fundus but also localises the area of pathology. Ten fluorescein images were analysed using seven feature descriptors including area, perimeter, circularity, curvature, entropy, wavelet second moment and the correlation dimension. Our results indicate that traditional features such as area or perimeter measures of neovascularisation associated with proliferative retinopathy were not sensitive enough to detect early proliferative retinopathy (SNR = 0.76, 0.75 respectively). The wavelet second moment provided the best discrimination with a SNR of 1.17. Combining second moment, curvature and global correlation dimension provided a 100% discrimination (SNR = 1)

Comparison of various methods to delineate blood vessels in retinal images

The blood vessels in the human retina are easily visualisable via digital fundus photography and provide an excellent window to the health of a patient affected by diseases of blood circulation such as diabetes. Diabetic retinopathy is identifiable through lesions of the vessels such as narrowing of the arteriole walls, beading of venules into sausage like structures and new vessel growth as an attempt to reperfuse ischaemic regions. Automated quantification of these lesions would be beneficial to diabetes research and to clinical practice, particularly for eye-screening programmes for the detection of eye-disease amongst diabetic persons

Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification

We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or non-vessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and continuous two-dimensional Morlet wavelet transform responses taken at multiple scales. The Morlet wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces and compare its performance with the linear minimum squared error classifier. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE and STARE databases of manually labeled non-mydriatic images. On the DRIVE database, it achieves an area under the receiver operating characteristic (ROC) curve of 0.9598, being slightly superior than that presented by the method of Staal et al.Comment: 9 pages, 7 figures and 1 table. Accepted for publication in IEEE Trans Med Imag; added copyright notic

Development of retinal blood vessel segmentation methodology using wavelet transforms for assessment of diabetic retinopathy

Automated image processing has the potential to assist in the early detection of diabetes, by detecting changes in blood vessel diameter and patterns in the retina. This paper describes the development of segmentation methodology in the processing of retinal blood vessel images obtained using non-mydriatic colour photography. The methods used include wavelet analysis, supervised classifier probabilities and adaptive threshold procedures, as well as morphology-based techniques. We show highly accurate identification of blood vessels for the purpose of studying changes in the vessel network that can be utilized for detecting blood vessel diameter changes associated with the pathophysiology of diabetes. In conjunction with suitable feature extraction and automated classification methods, our segmentation method could form the basis of a quick and accurate test for diabetic retinopathy, which would have huge benefits in terms of improved access to screening people for risk or presence of diabetes

A Review of the External Validity of Clinical Trials with Beta-Blockers in Heart Failure

This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License.Background: Beta-blockers (BBs) are the mainstay prognostic medication for all stages of chronic heart failure (CHF). There are many classes of BBs, each of which has varying levels of evidence to support its efficacy in CHF. However, most CHF patients have one or more comorbid conditions such as diabetes, renal impairment, and/or atrial fibrillation. Patient enrollment to randomized controlled trials (RCTs) often excludes those with certain comorbidities, particularly if the symptoms are severe. Consequently, the extent to which evidence drawn from RCTs is generalizable to CHF patients has not been well described. Clinical guidelines also underrepresent this point by providing generic advice for all patients. The aim of this review is to examine the evidence to support the use of BBs in CHF patients with common comorbid conditions. Methods: We searched MEDLINE, PubMed, and the reference lists of reviews for RCTs, post hoc analyses, systematic reviews, and meta-analyses that report on use of BBs in CHF along with patient demographics and comorbidities. Results: In total, 38 studies from 28 RCTs were identified, which provided data on six BBs against placebo or head to head with another BB agent in ischemic and nonischemic cardiomyopathies. Several studies explored BBs in older patients. Female patients and non-Caucasian race were underrepresented in trials. End points were cardiovascular hospitalization and mortality. Comorbid diabetes, renal impairment, or atrial fibrillation was detailed; however, no reference to disease spectrum or management goals as a focus could be seen in any of the studies. In this sense, enrollment may have limited more severe grades of these comorbidities. Conclusions: RCTs provide authoritative information for a spectrum of CHF presentations that support guidelines. RCTs may provide inadequate information for more heterogeneous CHF patient cohorts. Greater Phase IV research may be needed to fill this gap and inform guidelines for a more global patient population

Comparison between the Performances of a Bubble Pump Generator with Inlets Configurations

Abstract -The compressor or a mechanical pump of traditional refrigeration cycles is replaced by the bubble pump that circulates the binary working fluid in the diffusion absorption refrigeration (DAR) cycle. The bottleneck of the DAR cycle is the bubble pump, and its performance is determined by the amount of separated refrigerant that leaves the bubble pump. This work investigated the influence of the structure of the generator on the amount of desorbed refrigerant in a bubble pump system, with three parallel lifting tubes. Two modular experimental systems were designed to characterize the performance of three parallel bubble pump lift tubes with an environmentally-friendly binary solution of R134a-DMAC. The difference between the systems was in the design of the generator. One generator had three inlets and three outlets, and another one had a common inlet and three outlets. All other operating conditions remained the same. The results showed that an experimental system containing three inlets to the generator, produced more refrigerant than the system containing a common inlet

Species D Adenoviruses as Oncolytics against B-cell Cancers

Purpose: Oncolytic viruses are self-amplifying anticancer agents that make use of the natural ability of viruses to kill cells. Adenovirus serotype 5 (Ad5) has been extensively tested against solid cancers, but less so against B-cell cancers because these cells do not generally express the coxsackie and adenoviral receptor (CAR). To determine whether other adenoviruses might have better potency, we mined the adenovirus virome of 55 serotypes for viruses that could kill B-cell cancers. Experimental Design: Fifteen adenoviruses selected to represent Ad species B, C, D, E, and F were tested in vitro against cell lines and primary patient B-cell cancers for their ability to infect, replicate in, and kill these cells. Select viruses were also tested against B-cell cancer xenografts in immunodeficient mice. Results: Species D adenoviruses mediated most robust killing against a range of B-cell cancer cell lines, against primary patient marginal zone lymphoma cells, and against primary patient CD138þ myeloma cells in vitro. When injected into xenografts in vivo, single treatment with select species D viruses Ad26 and Ad45 delayed lymphoma growth. Conclusions: Relatively unstudied species Dadenoviruses have a unique ability to infect and replicate in B-cell cancers as compared with other adenovirus species. These data suggest these viruses have unique biology in B cells and support translation of novel species D adenoviruses as oncolytics against B-cell cancers

Hormone-dependent, CARM1-directed, arginine-specific methylation of histone H3 on a steroid-regulated promoter

AbstractActivation of gene transcription involves chromatin remodeling by coactivator proteins that are recruited by DNA-bound transcription factors. Local modification of chromatin structure at specific gene promoters by ATP-dependent processes and by posttranslational modifications of histone N-terminal tails provides access to RNA polymerase II and its accompanying transcription initiation complex [1, 2]. While the roles of lysine acetylation, serine phosphorylation, and lysine methylation of histones in chromatin remodeling are beginning to emerge [2–5], low levels of arginine methylation of histones have only recently been documented [4, 6–9], and its physiological role is unknown. The coactivator CARM1 methylates histone H3 at Arg17 and Arg26 in vitro [7] and cooperates synergistically with p160-type coactivators (e.g., GRIP1, SRC-1, ACTR) and coactivators with histone acetyltransferase activity (e.g., p300, CBP) to enhance gene activation by steroid and nuclear hormone receptors (NR) in transient transfection assays [10, 11]. In the current study, CARM1 cooperated with GRIP1 to enhance steroid hormone-dependent activation of stably integrated mouse mammary tumor virus (MMTV) promoters, and this coactivator function required the methyltransferase activity of CARM1. Chromatin immunoprecipitation assays and immunofluorescence studies indicated that CARM1 and the CARM1-methylated form of histone H3 specifically associated with a large tandem array of MMTV promoters in a hormone-dependent manner. Thus, arginine-specific histone methylation by CARM1 is an important part of the transcriptional activation process

A hybrid feature selection with ensemble classification for imbalanced healthcare data: a case study for brain tumor diagnosis

Electronic health records (EHRs) are providing increased access to healthcare data that can be made available for advanced data analysis. This can be used by the healthcare professionals to make a more informed decision providing improved quality of care. However, due to the inherent heterogeneous and imbalanced characteristics of medical data from EHRs, data analysis task faces a big challenge. In this paper, we address the challenges of imbalanced medical data about a brain tumor diagnosis problem. Morphometric analysis of histopathological images is rapidly emerging as a valuable diagnostic tool for neuropathology. Oligodendroglioma is one type of brain tumor that has a good response to treatment provided the tumor subtype is recognized accurately. The genetic variant, 1p-/19q-, has recently been found to have high chemosensitivity, and has morphological attributes that may lend it to automated image analysis and histological processing and diagnosis. This paper aims to achieve a fast, affordable, and objective diagnosis ofthis genetic variant of oligodendroglioma with a novel data mining approach combining a feature selection and ensemble-based classification. In this paper, 63 instances of brain tumor with oligodendroglioma are obtained due to prevalence and incidence of the tumor variant. In order to minimize the effect of an imbalanced healthcare data set, a global optimization-based hybrid wrapper-filter feature selection with ensemble classification is applied. The experiment results show that the proposed approach outperforms the standard techniques used in brain tumor classification problem to overcome the imbalanced characteristics of medical data