A grey wolf-based method for mammographic mass classification

Breast cancer is one of the most prevalent cancer types with a high mortality rate in women worldwide. This devastating cancer still represents a worldwide public health concern in terms of high morbidity and mortality rates. The diagnosis of breast abnormalities is challenging due to different types of tissues and textural variations in intensity. Hence, developing an accurate computer-aided system (CAD) is very important to distinguish normal from abnormal tissues and define the abnormal tissues as benign or malignant. The present study aims to enhance the accuracy of CAD systems and to reduce its computational complexity. This paper proposes a method for extracting a set of statistical features based on curvelet and wavelet sub-bands. Then the binary grey wolf optimizer (BGWO) is used as a feature selection technique aiming to choose the best set of features giving high performance. Using public dataset, Digital Database for Screening Mammography (DDSM), different experiments have been performed with and without using the BGWO algorithm. The random forest classifier with 10-fold cross-validation is used to achieve the classification task to evaluate the selected set of features’ capability. The obtained results showed that when the BGWO algorithm is used as a feature selection technique, only 30.7% of the total features can be used to detect whether a mammogram image is normal or abnormal with ROC area reaching 1.0 when the fusion of both curvelet and wavelet features were used. In addition, in case of diagnosing the mammogram images as benign or malignant, the results showed that using BGWO algorithm as a feature selection technique, only 38.5% of the total features can be used to do so with high ROC area result at 0.871

Renewable estimation and incremental inference in generalized linear models with streaming data sets

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153655/1/rssb12352_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153655/2/rssb12352-sup-0001-Supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153655/3/rssb12352.pd

HCV Infection among Saudi Population: High Prevalence of Genotype 4 and Increased Viral Clearance Rate

HCV is a major etiological agent of liver disease with a high rate of chronic evolution. The virus possesses 6 genotypes with many subtypes. The rate of spontaneous clearance among HCV infected individuals denotes a genetic determinant factor. The current study was designed in order to estimate the rate of HCV infection and ratio of virus clearance among a group of infected patients in Saudi Arabia from 2008 to 2011. It was additionally designed to determine the genotypes of the HCV in persistently infected patients. HCV seroprevalence was conducted on a total of 15,323 individuals. Seropositive individuals were tested by Cobas AmpliPrep/Cobas TaqMan HCV assay to determine the ratio of persistently infected patients to those who showed spontaneous viral clearance. HCV genotyping on random samples from persistently infected patients were conducted based on the differences in the 5′untranslated region (5′UTR). Anti-HCV antibodies were detected in 7.3% of the totally examined sera. A high percentage of the HCV infected individuals experienced virus clearance (48.4%). HCV genotyping revealed the presence of genotypes 1 and 4, the latter represented 97.6% of the tested strains. Evidences of the widespread of the HCV genotype 4 and a high rate of HCV virus clearance were found in Saudi Arabia

Involvement of the exomer complex in the polarized transport of Ena1 required for Saccharomyces cerevisiae survival against toxic cations

[EN] Exomer is an adaptor complex required for the direct transport of a selected number of cargoes from the trans-Golgi network (TGN) to the plasma membrane in Saccharomyces cerevisiae However, exomer mutants are highly sensitive to increased concentrations of alkali metal cations, a situation that remains unexplained by the lack of transport of any known cargoes. Here we identify several HAL genes that act as multicopy suppressors of this sensitivity and are connected to the reduced function of the sodium ATPase Ena1. Furthermore, we find that Ena1 is dependent on exomer function. Even though Ena1 can reach the plasma membrane independently of exomer, polarized delivery of Ena1 to the bud requires functional exomer. Moreover, exomer is required for full induction of Ena1 expression after cationic stress by facilitating the plasma membrane recruitment of the molecular machinery involved in Rim101 processing and activation of the RIM101 pathway in response to stress. Both the defective localization and the reduced levels of Ena1 contribute to the sensitivity of exomer mutants to alkali metal cations. Our work thus expands the spectrum of exomer-dependent proteins and provides a link to a more general role of exomer in TGN organization.We acknowledge Emma Keck for English language revision. We also thank members of the Translucent group, J. Arino, J. Ramos, and L. Yenush, for many useful discussions throughout this work and especially L. Yenush for her generous gift of strains and reagents. The help of O. Vincent was essential for developing the work involving RIM101. We also thank R. Valle for her technical assistance at the CR Laboratory. M. Trautwein is acknowledged for data acquisition and discussions during the early stages of the project. C.A. is supported by a USAL predoctoral fellowship. Work at the Spang laboratory was supported by the University of Basel and the Swiss National Science Foundation (31003A-141207 and 310030B-163480). 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A Fine-Grained Random Forests using Class Decomposition

Class decomposition describes the process of segmenting each class into a number of homogeneous subclasses. This can be naturally achieved through clustering. Utilising class decomposition can provide a number of benefits to supervised learning, especially ensembles. It can be a computationally efficient way to provide a linearly separable dataset without the need for feature engineering required by techniques like Support Ve]ctor Machines (SVM) and Deep Learning. For ensembles, the decomposition is a natural way to increase diversity; a key factor for the success of ensemble classifiers. In this paper, we propose to adopt class decomposition to the state-of-the-art ensemble learning Random Forests. Medical data for patient diagnosis may greatly benefit from this technique, as the same disease can have a diverse of symptoms. We have experimentally validated our proposed method on a number of datasets in that are mainly related to the medical domain. Results reported in this paper shows clearly that our method has significantly improved the accuracy of Random Forests

Constraining Nonstandard Neutrino-Electron Interactions due to a New Light Spin-1 Boson

We consider nonstandard interactions of neutrinos with electrons arising from a new light spin-1 particle with mass of tens of GeV or lower and couplings to the neutrinos and electron. This boson is not necessarily a gauge boson and is assumed to have no mixing with standard-model gauge bosons. Adopting a model-independent approach, we study constraints on the flavor-conserving and -violating couplings of the boson with the leptons from a number of experimental data. Specifically, we take into account the (anti)neutrino-electron scattering and e^+ e^- -> nu nubar gamma measurements and keep explicitly the dependence on the new particle mass in all calculations. We find that one of the two sets of data can provide the stronger constraints, depending on the mass and width of the boson. Also, we evaluate complementary constraints on its separate flavor-conserving couplings to the electron and neutrinos from other latest experimental results.Comment: 24 pages, 8 figures, expanded including supplementary experimental constraints, references added, matches journal versio