Inherited metabolic disorders (IMDs) are a diverse group of hereditary abnormalities that leads to a defect in metabolic pathway. Its diagnosis has been transformed by the innovations of molecular genetics and computational biology. Conventionally, diagnosis of IMDs is dependent on clinical findings and biochemical tests. Yet, these methods are limited due to a heterogeneity of such disorders and a large number of genes involved. The main objective of this review is to highlight the role of next-generation sequencing (NGS), including targeted gene panels, whole-exome sequencing (WES), and whole-genome sequencing (WGS), in the diagnosis of IMDs and providing reliable information in identifying genetic causes, and to explore the integrated analysis of several molecular layers such as genomics, transcriptomics, proteomics, metabolomics, and epigenetics. Targeted mass spectrometry and untargeted metabolomics methods are essential approaches for screening and identifying the metabolic patterns that act as a diagnosis biomarker to confirm the biochemical phenotypes associated with IMDs. Moreover, a new diagnostic model has been developed from the combination data of transcriptomics and proteomics to determine whether a gene mutation leads to a protein's dysfunction or not. The review concludes that the IMDs diagnosis should be lied in a fully integrated between molecular genetics techniques with multi-omics pipeline enhanced by artificial intelligence (AI) and machine learning (ML), which will provide a more rapid, accurate, and accessible path to diagnosis and, ultimately, more effective treatment.
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Transmission lines are generally subjected to faults, so it is advantageous to determine these faults as quickly as possible. This study uses an Artificial Neural Network technique to locate a fault as soon as it happens on the Doukan-Erbil of 132kv double Transmission lines network. CYME 7.1-Programming/Simulink utilized simulation to model the suggested network. A multilayer perceptron feed-forward artificial neural network with a back propagation learning algorithm is used for the intelligence locator's training, testing, assessment, and validation. Voltages and currents were applied as inputs during the neural network's training. The pre-fault and post-fault values determined the scaled values. The neural network's p
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Rapid worldwide urbanization and drastic population growth have increased the demand for new road construction, which will cause a substantial amount of natural resources such as aggregates to be consumed. The use of recycled concrete aggregate could be one of the possible ways to offset the aggregate shortage problem and reduce environmental pollution. This paper reports an experimental study of unbound granular material using recycled concrete aggregate for pavement subbase construction. Five percentages of recycled concrete aggregate obtained from two different sources with an originally designed compressive strength of 20–30 MPa as well as 31–40 MPa at three particle size levels, i.e., coarse, fine, and extra fine, were test
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This paper proposes a new approach, of Clustering Ultrasound images using the Hybrid Filter (CUHF) to determine the gender of the fetus in the early stages. The possible advantage of CUHF, a better result can be achieved when fuzzy c-mean FCM returns incorrect clusters. The proposed approach is conducted in two steps. Firstly, a preprocessing step to decrease the noise presented in ultrasound images by applying the filters: Local Binary Pattern (LBP), median, median and discrete wavelet (DWT),(median, DWT & LBP) and (median & Laplacian) ML. Secondly, implementing Fuzzy C-Mean (FCM) for clustering the resulted images from the first step. Amongst those filters, Median & Laplace has recorded a better accuracy. Our experimental evaluation on re
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