Positive vectors clustering using inverted Dirichlet finite mixture models

In this work we present an unsupervised algorithm for learning finite mixture models from multivariate positive data. Indeed, this kind of data appears naturally in many applications, yet it has not been adequately addressed in the past. This mixture model is based on the inverted Dirichlet distribution, which offers a good representation and modeling of positive non-Gaussian data. The proposed approach for estimating the parameters of an inverted Dirichlet mixture is based on the maximum likelihood (ML) using Newton Raphson method. We also develop an approach, based on the minimum message length (MML) criterion, to select the optimal number of clusters to represent the data using such a mixture. Experimental results are presented using artificial histograms and real data sets. The challenging problem of software modules classification is investigated within the proposed statistical framework, also.     

Mechanistic Studies on the [2 + 2] Photocycloaddition of Deuterium-Labeled Olefins

The mechanism of intramolecular [2 + 2] photocycloaddition of 1b has been investigated. Structure determination of deuterium-labeled photoadducts was accomplished by ²H NMR.     

A nonparametric Bayesian learning model using accelerated variational inference and feature selection

Pattern Analysis and Applications - Developing effective machine learning methods for multimedia data modeling continues to challenge computer vision scientists. The capability of providing...     

Online variational learning of finite inverted Beta-Liouville mixture model for biomedical analysis

Image segmentation is widely applied for biomedical image analysis. However, segmentation of medical images is challenging due to many image modalities, such as, CT, X-ray, MRI, microscopy among others. An additional challenge to this is the high variability, inconsistent regions with missing edges, absence of texture contrast, and high noise in the background of biomedical images. Thus, many segmentation approaches have been investigated to address these issues and to transform medical images into meaningful information. During the past decade, finite mixture models have been revealed to be one of the most flexible and popular approaches in data clustering. In this article, we propose a statistical framework for online variational learning of finite inverted Beta-Liouville mixture model for clustering medical images. The online variational learning framework is used to estimate the parameters and the number of mixture components simultaneously, thus decreasing the computational complexity of the model. To this end, we evaluated our proposed algorithm on five different biomedical image data sets including optic disc detection and localization in diabetic retinopathy, digital imaging in melanoma lesion detection and segmentation, brain tumor detection, colon cancer detection and computer aid detection (CAD) of Malaria. Furthermore, we compared the proposed algorithm with three other popular algorithms. In our results, we analyze that the proposed online variational learning of finite IBL mixture model algorithm performs accurately on multiple modalities of medical images. It detects the disease patterns with high confidence. Computational and statistical approaches like the one presented in this article hold a significant impact on medical image analysis and interpretation in both clinical applications and scientific research. We believe that the proposed algorithm has the capacity to address multi modal biomedical image data sets and can be further applied by researchers to analyze correct disease patterns.     

Image and video denoising by combining unsupervised bounded generalized gaussian mixture modeling and spatial information

Multimedia Tools and Applications - In recent years, a great deal of effort has been expended on developing robust solutions for images quality degradation caused mainly by noise. In this paper, we...     

Maternal N-Acetyl-Cysteine Prevents Neonatal Hypoxia-Induced Brain Injury in a Rat Model

Perinatal hypoxia is a major cause of infant brain damage, lifelong neurological disability, and infant mortality. N-Acetyl-Cysteine (NAC) is a powerful antioxidant that acts directly as a scavenger of free radicals. We hypothesized that maternal-antenatal and offspring-postnatal NAC can protect offspring brains from hypoxic brain damage.Sixty six newborn rats were randomized into four study groups. Group 1: Control (CON) received no hypoxic intervention. Group 2: Hypoxia (HYP)-received hypoxia protocol. Group 3: Hypoxia-NAC (HYP-NAC). received hypoxia protocol and treated with NAC following each hypoxia episode. Group 4: NAC Hypoxia (NAC-HYP) treated with NAC during pregnancy, pups subject to hypoxia protocol. Each group was evaluated for: neurological function (Righting reflex), serum proinflammatory IL-6 protein levels (ELISA), brain protein levels: NF-κB p65, neuronal nitric oxide synthase (nNOS), TNF-α, and IL-6 (Western blot) and neuronal apoptosis (histology evaluation with TUNEL stain). Hypoxia significantly increased pups brain protein levels compared to controls. NAC administration to dams or offspring demonstrated lower brain NF-κB p65, nNOS, TNF-α and IL-6 protein levels compared to hypoxia alone. Hypoxia significantly increased brain apoptosis as evidenced by higher grade of brain TUNEL reaction. NAC administration to dams or offspring significantly reduce this effect. Hypoxia induced acute sensorimotor dysfunction. NAC treatment to dams significantly attenuated hypoxia-induced acute sensorimotor dysfunction. Prophylactic NAC treatment of dams during pregnancy confers long-term protection to offspring with hypoxia associated brain injury, measured by several pathways of injury and correlated markers with pathology and behavior. This implies we may consider prophylactic NAC treatment for patients at risk for hypoxia during labor.     

Fly Ash-based Geopolymer Lightweight Concrete Using Foaming Agent

In this paper, we report the results of our investigation on the possibility of producing foam concrete by using a geopolymer system. Class C fly ash was mixed with an alkaline activator solution (a mixture of sodium silicate and NaOH), and foam was added to the geopolymeric mixture to produce lightweight concrete. The NaOH solution was prepared by dilute NaOH pellets with distilled water. The reactives were mixed to produce a homogeneous mixture, which was placed into a 50 mm mold and cured at two different curing temperatures (60 °C and room temperature), for 24 hours. After the curing process, the strengths of the samples were tested on days 1, 7, and 28. The water absorption, porosity, chemical composition, microstructure, XRD and FTIR analyses were studied. The results showed that the sample which was cured at 60 °C (LW2) produced the maximum compressive strength for all tests, (11.03 MPa, 17.59 MPa, and 18.19 MPa) for days 1, 7, and 28, respectively. Also, the water absorption and porosity of LW2 were reduced by 6.78% and 1.22% after 28 days, respectively. The SEM showed that the LW2 sample had a denser matrix than LW1. This was because LW2 was heat cured, which caused the geopolymerization rate to increase, producing a denser matrix. However for LW1, microcracks were present on the surface, which reduced the compressive strength and increased water absorption and porosity.     

Role of Glycosylation in Vascular Calcification

Glycosylation is an important step in post-translational protein modification. Altered glycosylation results in an abnormality that causes diseases such as malignancy and cardiovascular diseases. Recent emerging evidence highlights the importance of glycosylation in vascular calcification. Two major types of glycosylation, N-glycosylation and O-glycosylation, are involved in vascular calcification. Other glycosylation mechanisms, which polymerize the glycosaminoglycan (GAG) chain onto protein, resulting in proteoglycan (PG), also have an impact on vascular calcification. This paper discusses the role of glycosylation in vascular calcification.