Recently, more than 1000 large intergenic noncoding RNAs (lincRNAs) have been reported. These RNAs are evolutionarily conserved in mammalian genomes and thus presumably function in diverse biological processes. Here, we report the identification of lincRNAs that are regulated by p53. One of these lincRNAs (lincRNA-p21) serves as a repressor in p53-dependent transcriptional responses. Inhibition of lincRNA-p21 affects the expression of hundreds of gene targets enriched for genes normally repressed by p53. The observed transcriptional repression by lincRNA-p21 is mediated through the physical association with hnRNP-K. This interaction is required for proper genomic localization of hnRNP-K at repressed genes and regulation of p53 mediates apoptosis. We propose a model whereby transcription factors activate lincRNAs that serve as key repressors by physically associating with repressive complexes and modulate their localization to sets of previously active genes.

A Large Intergenic Noncoding RNA Induced by p53 Mediates Global Gene Repression in the p53 Response

Degradation of antibiotics by advanced oxidation processes: An overview.

An iron oxychloride (FeOCl) catalyst was developed for oxidative degradation of persistent organic compounds in aqueous solutions. Exceptionally high activity for the production of hydroxyl radical (OH·) by H2O2 decomposition was achieved, being 2–4 orders of magnitudes greater than that over other Fe-based heterogeneous catalysts. The relationship of catalyst structure and performance has been established by using multitechniques, such as XRD, HRTEM, and EPR. The unique structural configuration of iron atoms and the reducible electronic properties of FeOCl are responsible for the excellent activity. This study paves the way toward the rational design of relevant catalysts for applications, such as wastewater treatment, soil remediation, and other emerging environmental problems.

Iron Oxychloride (FeOCl): An Efficient Fenton-Like Catalyst for Producing Hydroxyl Radicals in Degradation of Organic Contaminants

COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively.

/pdf/new-machine-learning-method-for-image-based-diagnosis-of-31bh2ykn6w.pdf

New machine learning method for image-based diagnosis of COVID-19.

The American Cancer Society (ACS) recommends women aged 40 and above to have a mammogram every year and calls it a gold standard for breast cancer detection. Early detection of breast cancer can improve survival rates to a great extent. Inter-observer and intra-observer errors occur frequently in analysis of medical images, given the high variability between interpretations of different radiologists. Also, the sensitivity of mammographic screening varies with image quality and expertise of the radiologist. So, there is no golden standard for the screening process. To offset this variability and to standardize the diagnostic procedures, efforts are being made to develop automated techniques for diagnosis and grading of breast cancer images. A few papers have documented the general trend of computer-aided diagnosis of breast cancer, making a broad study of the several techniques involved. But, there is no definitive documentation focusing on the mathematical techniques used in breast cancer detection. This review aims at providing an overview about recent advances and developments in the field of Computer-Aided Diagnosis (CAD) of breast cancer using mammograms, specifically focusing on the mathematical aspects of the same, aiming to act as a mathematical primer for intermediates and experts in the field.

Computer-Aided Breast Cancer Detection Using Mammograms: A Review

The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process

A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation

A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram

http://ee.sharif.edu/~miap/paper/89022017.pdf

Breast cancer diagnosis in digital mammogram using multiscale curvelet transform.

Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.

https://www.mdpi.com/2075-4418/11/8/1390/pdf

Mohamed Meselhy Eltoukhy

Papers

The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process

A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation

A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram

Breast cancer diagnosis in digital mammogram using multiscale curvelet transform.

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review