It is generally believed that sequential and coordinated cascade of macrophages is essential for not only removal of damaged tissues but also their repair. The gastric mucosa comprises significant number of connective tissue cells including macrophages. It is not known whether these macrophages play a role in the activation of gastric stem cells and regeneration of gastric mucosa following injury. To analyse the effects of macrophages on gastric stem cell.To obtain macrophages from hematopoietic stem cells, whole bone marrow cells were isolated from both the femur and tibia of mice and cultured in the presence of macrophage colony stimulating factor in a non-cell culture treated petri dishes. Macrophages were treated with lipopolysaccharide (LPS) or IL-4 to induce their both pro-inflammatory and anti-inflammatory phenotypes, respectively. Condition media from these macrophage phenotypes were collected and added to cultured primary gastric stem cells for 24 hours. Then RNA was extracted from the stem cells and processed for RT-qPCR. While pro-inflammatory macrophage condition media had no effect of gastric stem cells, media obtained from cultured anti-inflammatory macrophages showed interesting effects. There was an induction of mRNA expression of genes specific for mucous (MUC6, TFF2) parietal (ATP4A) and endocrine (CHGA) cell lineages. The anti-inflammatory macrophages produce factors that play an important role in inducing gastric stem cell differentiation. Defining these factors will improve our understanding of the biology of gastric stem cells in health and disease.
Machine Learning / Data Mining techniques have proven to be a key technology in data processing and analysis in many areas. In healthcare, we can use Machine Learning / Data Mining techniques to mine the data of medical records to make better sense of it, uncover new knowledge, and generally provide better and faster health services. Diabetes is one of the diseases that has been investigated using intelligent techniques. Still, diabetes-related research continues to take place, and the use of traditional as opposed to non-traditional diabetes Type-2 risk factors in prediction tools and decision support applications instead of normal blood tests is an open research problem. The objective of the research is to find the relationships (implications) between medical tests of diabetic patients and possible diabetes complications. Also, while many researchers focused on developing models that assist in predicting diabetes with accuracy higher than the baseline standards stated in the literature, the pre-mentioned objective stated is covered in a very limited scope. There is room for deeper investigation, and the topic needs to be further examined. This study aims at localizing the problem of diabetes type-2 Metabolic Syndrome complication diagnosis to GCC nationals and especially Emirati patients, taking into consideration common medical tests e.g., HbA1c, Fasting Glucose, Random Glucose, and Insulin; and Diabetes Complications e.g., Hyperthyroidism, Hypertension, Diabetic Foot and Metabolic Syndrome.
Photo Acoustic Imaging has been recently used for several purposes, the modality has gained interest in medical applications. Photo Acoustic Tomography (PAT) is a biomedical imaging technique that depends on photo acoustic effect; which is the formation of sound waves following light absorption in a tissue sample. It is quantified by measuring the formed sound with appropriate detectors such as sensors. Compressive sensing (CS) is a new paradigm which is capable of reconstructing signals from fewer number of measurements than suggested by Nyquist rate. The objective of this paper is to show how to apply the CS framework to form a full view PAT image with less number of sensors. In this paper, numerical simulations are done to reconstruct PA image using CS framework. A comparison is done in terms of speed and quality between three different CS algorithms which are Alternating Direction Method of Multipliers, l_1-MAGIC and CVX toolbox.
In this paper, a sensor is designed using a split ring resonator with the defected ground structure to classify three different types of breast cancer cell lines. The differentiation between the three types is based on measuring the reflection coefficient for each type.
The aim of this paper is to spotlight on a novel approach for energy transfer utilizing the gas pipelines. The new proposed system will be converting the electricity from renewable energy to potential energy in the form of pressure. The exported gas in the gas pipelines can be compressed to higher pressure to act as an energy transfer medium. Later on, along the pipeline the pressure can be converted back to electrical energy. The proposed system dynamics will be analyzed and optimized using TRNSYS and MATLAB .
The paper presents a novel ultra-low power, embedded, and wearable walk-cycle monitoring system with applications in areas such as health-care, robotics, sports medicine, physical therapy, prosthesis, and animal sports. Customized shoes with sensors continuously measure the forces, and an electronic digital assistant is used to analyze the acquired measurements in real time by employing an IMU free and self-synchronizing method in order to estimate weight and study motion patterns. To achieve ultra-low power operation, the human body is used as a communication medium between the sensors and the digital assistant. The single-channel behavior of the human body is accommodated with a novel, simple yet robust single-wire signaling technique, Pulsed-Index Communication (PIC), that significantly reduces the system footprint and overall power consumption as it eliminates the need for clock and data recovery. The system prototype has been rigorously and successfully tested.
Artificial neural networks are commonly known as Universal Approximators; a property immensely useful in system identification and control applications. Traditionally, neural networks are trained with gradient-descent backpropagation algorithms. However, these algorithms are computationally burdened and slow due to the calculation of error derivatives. As a result, the research focus has shifted to develop gradient-free neural algorithms. One famous approach is to incorporate Lyapunov Functions in network parameter optimization. In this paper, we briefly discuss and analyze one such recently developed algorithm from the point-of-view of its applicability in adaptive control paradigm. It has been found that with a few proposed modifications, this algorithm can work excellently as neuro-adaptive inverse controller.
The rapid adoption of systems using IoT technologies is poised to create many exposed systems with new security vulnerabilities. IoT applications from a variety of domains may face severe security holes. Significant security risks to IoT systems come from the large number of edge devices. Edge-devices are very small, wireless-enabled microcontrollers running primitive operating systems. Their resource-constrained nature in an IoT eco-system poses a challenge to many aspects of security. The primary objective is to provide recommendations to improve an IoT system's overall security profile with minimum impact to its operations. Key use-cases in various application areas of IoT will be used to conduct an experimental evaluation of IoT systems to detect vulnerabilities. Their impact on IoT systems will be investigated, and counter measures will be proposed. Power consumption and resource utilization data will be collected and analyzed, and various networking profiles including 2G, 4G, and Wi-Fi will be simulated.
Embedded systems are being aggressively integrated in every aspect of modern life, and their uses range from personal devices for everyday use and convenience to devices deployed in critical systems, such as autonomous vehicles, aircrafts and industrial control systems. An often neglected attribute of embedded systems is cybersecurity, which often leads to an expanded attack surface in the systems they are deployed. In this paper we present a novel attack vector that enables stealthy information leakage from an embedded system. Specifically, we leverage structural components present in modern embedded systems, namely the Device Tree Blob, to extract information about the hardware profile of the system. Utilizing this information, we introduce a stealthy attack that leaks information from arbitrary memory locations taking advantage of the Direct Memory Access (DMA) controller and existing side-channels.
Privacy requirements and the need for collaborative analysis has motivated a significant amount of research on anonymization techniques and privacy-aware analysis. Anonymization techniques are typically applied to data in order to retain the privacy of the data. Some anonymization techniques preserve certain distances and properties of the original data points without revealing compromising information about it which enables performing collaborative privacy-preserving analysis. However, typical Anonymization techniques require a lot of expertise and domain knowledge in order to be applied effectively because of the effects they have on certain properties of the data. In this paper we discuss the types of Anonymization techniques according to how it transformations the type of data.
