Numts are fragments of mitochondrial DNA (mtDNA) that have been translocated to the nucleus, where they can persist while their mitochondrial counterparts continue to rapidly evolve. Thus, numts represent 'molecular fossils' useful for comparison with mitochondrial variation, and are particularly suited for studies of the fast-evolving hypervariable segment of the mitochondrial control region (HV1). Here we used information from numts found in western gorillas (Gorilla gorilla) and eastern gorillas (Gorilla beringei) to estimate that these two species diverged about 1.3 million years ago (Ma), an estimate similar to recent calculations for the divergence of chimpanzee and bonobo. We also describe the sequence of a gorilla numt still possessing a segment lost from all contemporary gorilla mtDNAs. In contrast to that sequence, many numts of the HV1 are highly similar to authentic mitochondrial organellar sequences, making it difficult to determine whether purported mitochondrial sequences truly derive from that genome. We used all available organellar HV1 and corresponding numt sequences from gorillas in a phylogenetic analysis aimed at distinguishing these two types of sequences. Numts were found in several clades in the tree. This, in combination with the fact that only a limited amount of the extant variation in gorillas has been sampled, suggests that categorization of new sequences by the indirect means of phylogenetic comparison would be prone to uncertainty. We conclude that for taxa such as gorillas that contain numerous numts, direct approaches to the authentication of HV1 sequences, such as amplification strategies relying upon the circularity of the mtDNA molecule, remain necessary.
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Genes (Basel)
December 2024
Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia.
Background/objectives: The ~1.6 kb NBPF repeat units in neuroblastoma breakpoint family (NBPF) genes are specific to humans and are associated with cognitive capacity in higher primates. While the number of NBPF monomers/Olduvai sequences in humans is approximately 2-3 times greater than in great apes, the difference in copy number values of canonical NBPF 3mer Higher-order repeats (HORs)/Olduvai triplets between humans and great apes is substantially larger.
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January 2025
University of Ghana, P.O. Box 134, Legon-Accra, Ghana.
Sentiment analysis has become a difficult and important task in the current world. Because of several features of data, including abbreviations, length of tweet, and spelling error, there should be some other non-conventional methods to achieve the accurate results and overcome the current issue. In other words, because of those issues, conventional approaches cannot perform well and accomplish results with high efficiency.
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January 2025
School of Psychology and Neuroscience, University of St. Andrews, St. Andrews, KY16 9AJ, UK.
Chimpanzees excel at inference tasks which require that they search for a single food item from partial information. Yet, when presented with 2-item tasks which test the same inference operation, chimpanzees show a consistent breakdown in performance. Here we test a diverse zoo-housed cohort (n = 24) comprising all 4 great ape species under the classic 4-cup 2-item task, previously administered to children and chimpanzees, and a modified task administered to baboons.
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December 2024
Department of Electrical and Electronics Engineering, SR University, Warangal, Telangana, 506371, India.
Autonomous microgrids (ATMG), with green power sources, like solar and wind, require an efficient control scheme to secure frequency stability. The weather and locationally dependent behavior of the green power sources impact the system frequency imperfectly. This paper develops an intelligent, i.
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December 2024
Science and Research Branch, Islamic Azad University, Tehran, Iran.
The growing global demand for water and energy has created an urgent necessity for precise forecasting and management of these resources, especially in urban regions where population growth and economic development are intensifying consumption. Shenzhen, a rapidly expanding megacity in China, exemplifies this trend, with its water and energy requirements anticipated to rise further in the upcoming years. This research proposes an innovative Convolutional Neural Network (CNN) technique for forecasting water and energy consumption in Shenzhen, considering the intricate interactions among climate, socio-economic, and demographic elements.
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