Prevalence and Loads of Fecal Pollution Indicators and the Antibiotic Resistance Phenotypes of Escherichia coli in Raw Minced Beef in Lebanon

Meat is an important source of high biological value proteins as well as many vitamins and minerals. In Lebanon, beef meats, including raw minced beef, are among the most consumed of the meat products. However, minced beef meat can also be an important source of foodborne illnesses. This is of a major concern, because food safety in Lebanon suffers from well-documented challenges. Consequently, the prevalence and loads of fecal coliforms and Escherichia coli were quantified to assess the microbiological acceptability of minced beef meat in Lebanon. Additionally, antibiotic resistance phenotypes of the E. coli were determined in response to concerns about the emergence of resistance in food matrices in Lebanon. A total of 50 meat samples and 120 E. coli isolates were analyzed. Results showed that 98% and 76% of meat samples harbored fecal coliforms and E. coli above the microbial acceptance level, respectively. All E. coli were resistant to at least one antibiotic, while 35% of the isolates were multidrug-resistant (MDR). The results suggest that Lebanon needs to (1) update food safety systems to track and reduce the levels of potential contamination in important foods and (2) implement programs to control the proliferation of antimicrobial resistance in food systems

Three-dimensional image segmentation using tissue-like P system

Membrane computing (MC), which abstracts computational models from the structure and functioning of biological cells or population of cells in tissues, has served as a rich framework for handling many problems. Various types of P systems have been proposed in the literature to perform edge-based and region-based segmentation of two-dimensional digital images. However, less attention has been paid to the segmentation of three-dimensional (3D) medical images. Hence, the main contribution of this paper is to propose a tissue-like P system for segmenting 3D medical images. To the best of our knowledge, this is the first work that practically adapts MC for 3D images. Experimental results demonstrate the efficiency of the proposed approach in segmenting 3D images, and it has the potential to be used in real-world applications

Image segmentation using membrane computing: a literature survey

Membrane computing, a recent branch of natural computing, has been gaining momentum attention in the last few decades due to its massive parallelism and efficient computation. Many researchers in the field of membrane computing have proposed sophisticated techniques inspired by cell biology for computer science applications, especially when they considered cell organization in tissues, organs, and most recently, from the organization of neurons. The interdisciplinary applications of membrane computing include, but not limited to computer science, biology, biomedicine, bioinformatics and several other fields such as mathematics, artificial intelligence, automation, economics, to name but a few. Their applications are pertaining to computer graphics, approximate optimization, cryptography, parallel computing and image processing. Hence, in this paper we present an up to date comprehensive literature review pertaining to the application of membrane computing in the area of digital image analysis, especially image segmentation, comprehensively and systematically. We thoroughly investigate the recent advancement in the field of image segmentation using membrane system. Furthermore, we highlight the merits and demerits of various software tools and methods. Finally, we suggest some intuitive future directions in light of the current limitations