Microproteins encoded by small open reading frames (sORFs) have emerged as a fascinating frontier in genomics. Traditionally overlooked due to their small size, recent technological advancements such as ribosome profiling, mass spectrometry-based strategies and advanced computational approaches have led to the annotation of more than 7000 sORFs in the human genome. Despite the vast progress, only a tiny portion of these microproteins have been characterized and an important challenge in the field lies in identifying functionally relevant microproteins and understanding their role in different cellular contexts. In this review, we explore the recent advancements in sORF research, focusing on the new methodologies and computational approaches that have facilitated their identification and functional characterization. Leveraging these new tools hold great promise for dissecting the diverse cellular roles of microproteins and will ultimately pave the way for understanding their role in the pathogenesis of diseases and identifying new therapeutic targets.
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http://dx.doi.org/10.1016/j.isci.2024.108972 | DOI Listing |
JASA Express Lett
January 2025
Department of Imaging Sciences, University of Rochester, Rochester, New York 14642, USA.
Ultrasound tomography fundamentally relies on low-frequency data to avoid cycle skipping in full-waveform inversion (FWI). In the absence of sufficiently low-frequency data, we can extrapolate low-frequency content from existing high-frequency signals by using the same approach used in frequency-difference beamforming. This low-frequency content is then used to kickstart FWI and avoid cycle skipping at higher frequencies.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
Departement de Physique Theorique, Universite de Geneve, 24 quai Ernest Ansermet, 1211 Geneve 4, Switzerland.
We consider resonant wavelike dark matter conversion into low-frequency radio waves in the Earth's ionosphere. Resonant conversion occurs when the dark matter mass and the plasma frequency coincide, defining a range m_{DM}∼10^{-9}-10^{-8} eV where this approach is best suited. Owing to the nonrelativistic nature of dark matter and the typical variational scale of the Earth's ionosphere, the standard linearized approach to computing dark matter conversion is not suitable.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
Quantum Lab, Boehringer Ingelheim, 55218 Ingelheim am Rhein, Germany.
The phase estimation algorithm is crucial for computing the ground-state energy of a molecular electronic Hamiltonian on a quantum computer. Its efficiency depends on the overlap between the Hamiltonian's ground state and an initial state, which tends to decay exponentially with system size. We showcase a practical orbital optimization scheme to alleviate this issue.
View Article and Find Full Text PDFJ Forensic Odontostomatol
December 2024
Laboratory of Personal Identification and Forensic Morphology, Department of Health Sciences, University of Florence, Florence, Italy.
The age estimation of skeletal remains still represents a central issue not only for the reconstruction of the so-called "biological profile," but mostly for the palaeodemographic investigation. This research aims at verifying the feasibility of the adult age estimation method developed on living people by Pinchi et al. (2015 and 2018), for estimating the age at the death of 37 subjects from ancient populations found in two different Italian necropolis of archaeological interest (Mont'e Prama and Florence, X-IX century B.
View Article and Find Full Text PDFPLoS One
January 2025
School of Physical Education, Jinjiang College, Sichuan University, Chengdu, Sichuan Province, People's Republic of China.
In athletes' competitions and daily training, in order to further strengthen the athletes' sports level, it is usually necessary to analyze the athletes' sports actions at a specific moment, in which it is especially important to quickly and accurately identify the categories and positions of the athletes, sports equipment, field boundaries and other targets in the sports scene. However, the existing detection methods failed to achieve better detection results, and the analysis found that the reasons for this phenomenon mainly lie in the loss of temporal information, multi-targeting, target overlap, and coupling of regression and classification tasks, which makes it more difficult for these network models to adapt to the detection task in this scenario. Based on this, we propose for the first time a supervised object detection method for scenarios in the field of motion management.
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