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"Ahmad M."
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Plant breeding advancements with “CRISPR-Cas” genome editing technologies will assist future food security
Genome editing techniques are being used to modify plant breeding, which might increase food production sustainably by 2050. A product made feasible by genome editing is becoming better known, because of looser regulation and widespread acceptance. The world’s population and food supply would never have increased proportionally under current farming practices. The development of plants and food production has been greatly impacted by global warming and climate change. Therefore, minimizing these effects is crucial for agricultural production that is sustainable. Crops are becoming more resilient to abiotic stress because of sophisticated agricultural practices and a better understanding of the abiotic stress response mechanism. Both conventional and molecular breeding techniques have been used to create viable crop types both processes are time-consuming. Recently, plant breeders have shown an interest in genome editing approaches for genetic manipulation that use clustered regularly interspaced short palindromic repeats (CRISPR/Cas9). To ensure the security of the food supply in the future, plant kinds with desired traits must be developed. A completely new era in plant breeding has begun because of the revolution in genome editing techniques based on the CRISPR/CRISPR-associated nuclease (Cas9) systems. All plants may effectively target a particular gene or group of loci using Cas9 and single-guide RNA (sgRNA). CRISPR/Cas9 can thereby save time and labor compared to conventional breeding methods. An easy, quick, and efficient method for directly altering the genetic sequences in cells is with the CRISPR and Cas9 systems. The CRISPR-Cas9 system, which was developed from components of the earliest known bacterial immune system, allows for targeted gene breakage and gene editing in a variety of cells/RNA sequences to guide endonuclease cleavage specificity in the CRISPR-Cas9 system. Editing can be directed to practically any genomic site by altering the guide RNA (gRNA) sequence and delivering it to a target cell along with the Cas9 endonuclease. We summarize recent CRISPR/Cas9 plant research findings, investigate potential applications in plant breeding, and make predictions about likely future breakthroughs and approaches to food security through 2050.
Journal Article
Anxiety, depression, stress, fear and social support during COVID-19 pandemic among Jordanian healthcare workers
The emergence of Coronavirus disease 2019 (COVID-19) has affected health-care workers’ psychological and mental health. Few studies have been conducted examining the psychological effect of COVID-19 on health-care worker psychological health in Jordan. Therefore, the present study aims to assess the respective levels of fear, anxiety, depression, stress, social support, and the associated factors, experienced by Jordanian health-care workers during the COVID-19 Pandemic. This study adopted a cross-sectional, correlational design to collect data from 365 health-care workers in Amman, Jordan, from August 16th to 23rd, 2020. Along with collecting sociodemographic characteristics, the Fear of COVID-19 Scale, the Depression, Anxiety, Stress Scale, and the Multidimensional Scale of Perceived Social Support electronically administered to participants. The majority of the participants (69.3%) were registered nurses. The mean overall score for the Fear of COVID-19 scale was 23.64 (SD + 6.85) which again exceeded the mid-point for the total score range (21), indicating elevated level fear of the COVID-19 pandemic. Participants had displayed extremely severe depression 40%, extremely severe anxiety 60%, and 35% severely distressed. Scores for depression (21.30 ± 10.86), anxiety (20.37 ± 10.80), stress (23.33 ± 10.87) were also high. Factors determined to be associated with psychological distress were being male, married, aged 40 years and older, and having more clinical experience. Assessment of social support indicated moderate-to-high levels of perceived support for all dimensions (significant other: 5.17 ± 1.28, family: 5.03 ± 1.30, friends: 5.05 ± 1.30). Weak significant correlations were found between social support and the other study variables (r < 0.22), indicating a weak association with fear, depression, anxiety, and stress, respectively. Overall, Jordanian health-care workers sample reported fear, depression, anxiety, and stress. The associated factors were being male, married, aged 40 years and older, and having more clinical experience. Regarding social support, participants primarily relied on support from their families, followed by support from friends.
Journal Article
The genome editing revolution: review
Development of efficient strategies has always been one of the great perspectives for biotechnologists. During the last decade, genome editing of different organisms has been a fast advancing field and therefore has received a lot of attention from various researchers comprehensively reviewing latest achievements and offering opinions on future directions. This review presents a brief history, basic principles, advantages and disadvantages, as well as various aspects of each genome editing technology including the modes, applications, and challenges that face delivery of gene editing components. Despite the success already achieved, the genome editing techniques are still suffering certain difficulties. Challenges must be overcome before the full potential of genome editing can be realized.
Journal Article
Time to Revisit Existing Student's Performance Evaluation Approach in Higher Education Sector in a New Era of ChatGPT - A Case Study
Artificial intelligence-based tools are rapidly revolutionizing the field of higher education, yet to be explored in terms of their impact on existing higher education institutions' (HEIs) practices adopted for continuous learning improvement, given the sparsity of the literature and empirical experiments in undergraduate degree programs. After the entry of ChatGPT -a conversational artificial intelligence (AI) tool that uses a deep learning model to generate human-like text response based on provided input-it has become crucial for HEIs to be exposed to the implications of AI-based tools on students' learning outcomes, commonly measured using an assessment-based approach to improve program quality, teaching effectiveness, and other learning support. An empirical study has been conducted to test the ChatGPT capability of solving a variety of assignments (from different level courses of undergraduate degree programs) to compare its performance with the highest scored student(s). Further, the ChatGPT-generated assignments were tested using the best-known tools for plagiarism detection to determine whether they could pass the academic integrity tests, including Turnitin, GPTZero, and Copyleaks. The study reported the limitations of the Bot and highlighted the implications of the newly launched AI-based ChatGPT in academia, which calls for HEIs' managers and regulators to revisit their existing practices used to monitor students' learning progress and improve their educational programs.
Journal Article
A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease
Alzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.
Journal Article