Arabic prosody is the science that studies the music of Arabic poetry, which is mainly meter and rhyme. The identification of meters for Arabic verses or poems is a complicated task. This task requires a certain level of expertise to identify the meter to which a verse belongs. In this paper, we present BASRAH1, a system that automatically identifies the meter of Arabic poetry by using the numerical prosody method. The numerical prosody method depends on verse coding, which is derived from the general concept of Al-Khalil's feet by using two primary units (cord = 2) and (peg = 3). On testing both old and modern Arabic verses and poems, BASRAH has proved to be an efficient tool to help inexperienced users to determine the meter of Arabic verses and poems.
Reliable, specific polyclonal and monoclonal antibodies are important tools in research and medicine. However, discovery of antibodies against their targets in their native forms is difficult. Here, we present a novel method for discovery of antibodies against membrane proteins in their native configuration in mammalian cells. The method involves the co-expression of an antibody library in a population of mammalian cells that express the target polypeptide within a natural membrane environment on the cell surface. Cells that secrete a single-chain fragment variable (scFv) that binds to the target membrane protein thereby become self-labelled enabling enrichment and isolation by magnetic sorting and Förster resonance energy transfer (FRET)-based flow sorting. Library sizes of up to 109 variants can be screened, thus allowing campaigns of naïve scFv libraries to be selected against membrane protein antigens in a CHO cell system. We validate this method by screening a synthetic naïve human scFv library against CHO cells expressing the oncogenic target epithelial cell adhesion molecule (EpCAM) and identify a panel of three novel binders to this membrane protein, one with dissociation constant (KD) as low as 0.8nM. We further demonstrate that the identified antibodies have utility for killing EpCAM positive cells when used as a targeting domain on CAR-T cells. Thus, we provide a new tool for identifying novel antibodies that act against membrane proteins, which could catalyze the discovery of new candidates for antibody-based therapies.
Particulate matter (PM 2.5 ) concentrations are a serious human health concern and global models are the common methods for PM 2.5 particle estimation disregarding the local changes and factors. In this study, a polynomial model for PM 2.5 particles prediction was proposed to examine the correlations among PM 2.5 , PM 10 , and meteorological parameters. The study was carried out in the north of Iraq including two provinces; Kirkuk and Sulaymaniyah. The data gathered from different sources. Two datasets have been used, collected during July 2019 and February 2020. To test our methodology, the model was applied on a small subset of the study area (5.6 km 2 ) inside the Kirkuk province. Datasets (observation and ground truth) were utilized to examine the model. Based on the July 2019 dataset, the mean local R 2 values were estimated at 0.98 and 0.97 in the north part of Iraq, and inside the Kirkuk province (the small subset), respectively. While based on the February 2020 dataset, the mean local R 2 values were estimated at 0.98 inside the Kirkuk province. High values of prediction accuracies were obtained by 82% and 96% in July and February, respectively. Moreover, our findings highlighted that the health impacts and air quality varied from moderate to unhealthy in the region.
A Coronavirus disease 2019 (COVID-19) pandemic detection considers a critical and challenging task for the medical practitioner. The coronavirus disease spread so rapidly between people and infected more than one hundred and seventy million people worldwide. For this reason, it is necessary to detect infected people with coronavirus and take action to prevent virus spread. In this study, a COVID-19 classification methodology was adopted to detect infected people using computed tomography (CT) images. Deep learning was applied to recognize COVID-19 infected cases for different patients by employing deep features. This methodology can be beneficial for medical practitioners to diagnose infected patients. The results were based on a new data collection named BasrahDataset that includes different CT scan videos for Iraqi patients. The proposed system gave promised results with a 99% F1-score for detecting COVID-19.
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