Recycled Aggregate Self-curing High-strength Concrete

The use of recycled aggregates from demolished constructions as coarse aggregates for concrete becomes a need to reduce the negative effects on the environment. Internal curing is a technique that can be used to provide additional moisture in concrete for more effective hydration of cement to reduce the water evaporation from concrete, increase the water retention capacity of concrete compared to the conventionally cured concrete. High strength concrete as a special concrete type has a high strength with extra properties compared to conventional concrete. In this research, the combination of previous three concrete types to obtain self-curing high-strength concrete cast using coarse recycled aggregates is studied. The effect of varying water reducer admixture and curing agent dosages on both the fresh and hardened concrete properties is studied. The fresh properties are discussed in terms of slump values. The hardened concrete properties are discussed in terms of compressive, splitting tensile, flexure and bond strengths. The obtained results show that, the using of water reducer admixture enhances the main fresh and hardened properties of self-curing high-strength concrete cast using recycled aggregate. Also, using the suggested chemical curing agent increased the strength compared to conventional concrete without curing

Medical image analysis for the early prediction of hypertension

Recently, medical image analysis has become a vital evolving technology that is used in the early diagnosis of various diseases. Medical imaging techniques enable physicians to capture noninvasive images of structures inside the human body (such as bones, tissues, or blood vessels) as well as their functions (such as brain activity). In this study, magnetic resonance angiography (MRA) images have been analyzed to help physicians in the early prediction of hypertension. Hypertension is a progressive disease that may take several years before being fully understood. In the United States, hypertension afflicts one in every three adults and is a leading cause of mortality in more than half a million patients every year. Specific alterations in human brains’ cerebrovasculature have been observed to precede the onset of hypertension. This study presents a computer-aided diagnosis system (CAD) that can predict hypertension prior to the systemic onset of the disease. This MRA-based CAD system is able to detect, track, and quantify the hypertension-related cerebrovascular alterations, then it makes a decision based on the analyzed data about whether each subject is at a high risk of developing hypertension or not. Such kind of prediction can help clinicians in taking proactive and preventative steps to stop the progress of the disease and mitigate adverse events

Using 3-D CNNs and Local Blood Flow Information to Segment Cerebral Vasculature

© 2018 IEEE. The variability of the strength (increase or decrease) of the blood flow signals throughout the range of slices of the MRA volume is a big challenge for any segmentation approach because it introduces more inhomogenities to the MRA data and hence less accuracy. In this paper, a novel cerebral blood vessel segmentation framework using Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) is proposed to handle this challenge. The segmentation framework is based on using three dimensional convolutional neural networks (3D-CNN) to segment the cerebral blood vessels taking into account the variability of blood flow signals throughout the MRA scans. It consists of the following two steps: i) bias field correction to handle intensity inhomogeneity which are caused by magnetic settings, ii)instead of constructing one CNN model for the whole TOF-MRA brain, the TOF-MRA volume is divided into two compartments, above Circle of Willis (CoW) and at and below CoW to account for blood flow signals variability across the MRA volume\u27s slices, then feed these two volumes into the three dimensional convolutional neural networks (3D-CNN). The final segmentation result is the combination of the output of each model. The proposed framework is tested on in-vivo data (30 TOF-MRA data sets). Both qualitative and quantitative validation with respect to ground truth (delineated by an MRA expert) are provided. The proposed approach achieved a high segmentation accuracy with 84.37% Dice similarity coefficient, sensitivity of 86.14%, and specificity of 99.00%

ANTIFUNGAL AND ANTIHEPATOTOXIC EFFECTS OF SEPIA INK EXTRACT AGAINST OXIDATIVE STRESS AS A RISK FACTOR OF INVASIVE PULMONARY ASPERGILLOSIS IN NEUTROPENIC MICE.

Background: There is a great need for novel strategies to overcome the high mortality associated with invasive pulmonary aspergillosis (IPA) in immunocompromised patients. To evaluate the antifungal and antihepatotoxic potentials of Sepia ink extract, its effect on liver oxidative stress levels was analyzed against IPA in neutropenic mice using amphotercin B as a reference drug. Materials and Methods: Eighty neutropenic infected mice were randomly assigned into four main groups. The 1st group was treated with saline, neutropenic infected (NI), the 2nd group was treated with ink extract (200 mg/kg) (IE) and the 3rd group was treated with amphotericin B (150 mg/kg) (AMB) and 4th group was treated with IE plus AMB. Treatment was started at 24 h after fungal inoculation (1×109 conidia/ml). Results: The present study revealed good in vitro and in vivo antifungal activity of IE against A. fumigatus. IE significantly reduced hepatic fungal burden and returns liver function and histology to normal levels. Compared with the untreated infected group, mice in the IE, AMB, and IE+ AMB groups had increased glutathione reduced (GSH) and superoxide dismutase (SOD) and significantly reduced malondialdehyde (MDA) levels at 24 and 72 h after inoculation with A. fumigatus conidia. Conclusion: It is then concluded that in combination with antifungal therapy (AMB), IE treatment can reduce hepatic fungal burden, alleviate hepatic granulomatous lesions and oxidative stress associated with IPA in neutropenic mice

Accurate Segmentation of Cerebrovasculature from TOF-MRA Images Using Appearance Descriptors

© 2013 IEEE. Analyzing cerebrovascular changes can significantly lead to not only detecting the presence of serious diseases e.g., hypertension and dementia, but also tracking their progress. Such analysis could be better performed using Time-of-Flight Magnetic Resonance Angiography (ToF-MRA) images, but this requires accurate segmentation of the cerebral vasculature from the surroundings. To achieve this goal, we propose a fully automated cerebral vasculature segmentation approach based on extracting both prior and current appearance features that have the ability to capture the appearance of macro and micro-vessels in ToF-MRA. The appearance prior is modeled with a novel translation and rotation invariant Markov-Gibbs Random Field (MGRF) of voxel intensities with pairwise interaction analytically identified from a set of training data sets. The appearance of the cerebral vasculature is also represented with a marginal probability distribution of voxel intensities by using a Linear Combination of Discrete Gaussians (LCDG) that its parameters are estimated by using a modified Expectation-Maximization (EM) algorithm. The extracted appearance features are separable and can be classified by any classifier, as demonstrated by our segmentation results. To validate the accuracy of our algorithm, we tested the proposed approach on in-vivo data using 270 data sets, which were qualitatively validated by a neuroradiology expert. The results were quantitatively validated using the three commonly used metrics for segmentation evaluation: the Dice coefficient, the modified Hausdorff distance, and the absolute volume difference. The proposed approach showed a higher accuracy compared to two of the existing segmentation approaches

Novel Approaches For Segmenting Cerebral Vasculature

In this chapter, we propose two segmentation approaches that are able to segment cerebral vasculature automatically and accurately. This would potentially help experts in the early analysis and diagnosis of severe diseases, specifically, multiple sclerosis

Early Diagnosis System For Lung Nodules Based On The Integration Of A Higher-Order Mgrf Appearance Feature Model And 3d-Cnn

In this chapter, a new system for lung nodule diagnosis, using features extracted from one computed tomography (CT) scan, is presented. To get an accurate diagnosis of the detected lung nodules, the proposed framework integrates the following two groups of features: (i) appearance features that are modeled using higher-order Markov–Gibbs random field (MGRF)-model that has the ability to describe the spatial inhomogeneities inside the lung nodule; and (ii) local features that are extracted using 3D convolutional neural networks (3D-CNN) because of its ability to exploit the spatial correlation of input data in an efficient way. The novelty of this chapter is to accurately model the appearance of the detected lung nodules using a new developed 7th-order MGRF model that has the ability to model the existing spatial inhomogeneities for both small and large detected lung nodules, in addition to the integration with the extracted local features from 3D-CNN. Finally, a deep autoencoder (AE) classifier is fed by the above two feature groups to distinguish between the malignant and benign nodules

Studying the Role of Cerebrovascular Changes in Different Compartments in Human Brains in Hypertension Prediction

Hypertension is a major cause of mortality of millions of people worldwide. Cerebral vascular changes are clinically observed to precede the onset of hypertension. The early detection and quantification of these cerebral changes would help greatly in the early prediction of the disease. Hence, preparing appropriate medical plans to avoid the disease and mitigate any adverse events. This study aims to investigate whether studying the cerebral changes in specific regions of human brains (specifically, the anterior, and the posterior compartments) separately, would increase the accuracy of hypertension prediction compared to studying the vascular changes occurring over the entire brain’s vasculature. This was achieved by proposing a computer-aided diagnosis system (CAD) to predict hypertension based on cerebral vascular changes that occur at the anterior compartment, the posterior compartment, and the whole brain separately, and comparing corresponding prediction accuracy. The proposed CAD system works in the following sequence: (1) an MRA dataset of 72 subjects was preprocessed to enhance MRA image quality, increase homogeneity, and remove noise artifacts. (2) each MRA scan was then segmented using an automatic adaptive local segmentation algorithm. (3) the segmented vascular tree was then processed to extract and quantify hypertension descriptive vascular features (blood vessels’ diameters and tortuosity indices) the change of which has been recorded over the time span of the 2-year study. (4) a classification module used these descriptive features along with corresponding differences in blood pressure readings for each subject, to analyze the accuracy of predicting hypertension by examining vascular changes in the anterior, the posterior, and the whole brain separately. Experimental results presented evidence that studying the vascular changes that take place in specific regions of the brain, specifically the anterior compartment reported promising accuracy percentages of up to 90%. However, studying the vascular changes occurring over the entire brain still achieve the best accuracy (of up to 100%) in hypertension prediction compared to studying specific compartments

Detecting Oxides Mineralization Utilizing Remote Sensing and Comprehensive Mineralogical Analysis: A Case Study Around Mikbi-Zayatit District, South Eastern Desert, Egypt

Undoubtedly, involving more tools, datasets, and techniques for detecting the mineralized areas sharply narrow the zones to be investigated and delivered, in most cases highly potential zones. Consequently, this study is an attempt to apply remote sensing data including Sentinel 2 and ASTER, field observations, petrography of the hydrothermal alteration processes, ore microscopic investigations, X-ray examinations, and EDX analysis to detect and emphasize mineralization types at Wadi Mikbi and Wadi Zayatit district, South Eastern Desert, Egypt. Towards accurate lithological mapping, remote sensing data, previous geological maps, and the field investigations recorded serpentinites, ophiolitic metagabbros, amphibolites, epidosite, arc-related metasediments (schists and quartzites), metagabbro-tonalite complex, dunite, olivine gabbros, and granitic rocks encountered in the study district. Additionally, various hydrothermal alteration zones have been primarily outlined using ASTER and Sentinel 2 data by implementing informative band ratios and constrained energy minimization techniques. The mineralogical studies have confirmed most of the remotely-detected hydrothermal alteration minerals. Ore microscopy, XRD technique, and EDX microchemical analysis of representative mineralized samples of the study district identified magnetite, ilmenite, titanomagnetite, chromite, magnesioferrite, quartz, apatite, clinochlore, plagioclase, pyroxene and epidote. Cross-linking remote sensing results, field observations and laboratory studies revealed that the dominant hydrothermal alteration processes include oxidization, serpentinization, carbonatization, epidotization, silicification, zoisitization, muscovitization, sericitization, and chloritization. Spatial overlay analysis of the resultant altered features, structural dissection, field observations, and analytical studies were integrated to build a mineral potentiality map of the study district