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Fulltext Automatic barcode gap discovery reveals diverse clades of Rhipicephalus spp. and Haemaphysalis spp. ticks from small mammals in 'Asir, Saudi Arabia

Alghamdi SQ, Low VL, Alkathiry HA, Alagaili AN, McGarry JW, Makepeace BL

Parasit Vectors, 2021 Oct 19;14(1):541.
PMID: 34666829 DOI: 10.1186/s13071-021-05049-x

BACKGROUND: The ixodid tick genera Rhipicephalus and Haemaphysalis contain several species of medical and/or veterinary importance, but their diversity in some regions of the world remains under-explored. For instance, very few modern studies have been performed on the taxonomy of these genera on the Arabian Peninsula.
METHODS: In this study, we trapped small mammals in the 'Asir Mountains of south-western Saudi Arabia and collected tick specimens for morphological examination and molecular barcoding, targeting three mitochondrial loci: cox1, 16S rRNA and 12S rRNA.
RESULTS: We obtained a total of 733 ticks (608 Haemaphysalis spp. and 125 Rhipicephalus spp.) from 75 small mammal hosts belonging to six species. All tick specimens were immature except for nine adults recovered from a hedgehog (Paraechinus aethiopicus). Morphologically, the Rhipicephalus ticks resembled R. camicasi, but the Haemaphysalis ticks showed differences in palp morphology compared with species previously described from Saudi Arabia. Phylogenetic analysis and automatic barcode gap discovery identified a novel clade of Rhipicephalus sp. representing most of the nymphs. This was most closely related to R. leporis, R. guilhoni and R. linnaei. The adult ticks and a small proportion of nymphs clustered with R. camicasi sequences from a previous study. Finally, the Haemaphysalis nymphs formed two distinct clades that were clearly separated from all reference sequences but closest to some African species.
CONCLUSIONS: This apparent high level of tick diversity observed in a single study site of only ~ 170 km2, on a relatively small number of hosts, highlights the potential for the discovery of new tick species on the Arabian Peninsula.
Fulltext Digital System Design for Quantum Error Correction Codes

Khalifa OO, Amirah Bt Sharif N, Saeed RA, Abdel-Khalek S, Alharbi AN, Alkathiri AA

Contrast Media Mol Imaging, 2021;2021:1101911.
PMID: 34992507 DOI: 10.1155/2021/1101911

Quantum computing is a computer development technology that uses quantum mechanics to perform the operations of data and information. It is an advanced technology, yet the quantum channel is used to transmit the quantum information which is sensitive to the environment interaction. Quantum error correction is a hybrid between quantum mechanics and the classical theory of error-correcting codes that are concerned with the fundamental problem of communication, and/or information storage, in the presence of noise. The interruption made by the interaction makes transmission error during the quantum channel qubit. Hence, a quantum error correction code is needed to protect the qubit from errors that can be caused by decoherence and other quantum noise. In this paper, the digital system design of the quantum error correction code is discussed. Three designs used qubit codes, and nine-qubit codes were explained. The systems were designed and configured for encoding and decoding nine-qubit error correction codes. For comparison, a modified circuit is also designed by adding Hadamard gates.
Fulltext Data-driven audiogram classifier using data normalization and multi-stage feature selection

Elkhouly A, Andrew AM, Rahim HA, Abdulaziz N, Malek MFA, Siddique S

Sci Rep, 2023 Feb 01;13(1):1854.
PMID: 36725966 DOI: 10.1038/s41598-022-25411-y

Audiograms are used to show the hearing capability of a person at different frequencies. The filter bank in a hearing aid is designed to match the shape of patients' audiograms. Configuring the hearing aid is done by modifying the designed filters' gains to match the patient's audiogram. There are few problems faced in achieving this objective successfully. There is a shortage in the number of audiologists; the filter bank hearing aid designs are complex; and, the hearing aid fitting process is tiring. In this work, a machine learning solution is introduced to classify the audiograms according to the shapes based on unsupervised spectral clustering. The features used to build the ML model are peculiar and describe the audiograms better. Different normalization methods are applied and studied statistically to improve the training data set. The proposed Machine Learning (ML) algorithm outperformed the current existing models, where, the accuracy, precision, recall, specificity, and F-score values are higher. The reason for the better performance is the use of multi-stage feature selection to describe the audiograms precisely. This work introduces a novel ML technique to classify audiograms according to the shape, which, can be integrated to the future and existing studies to change the existing practices in classifying audiograms.

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