Search

Abstract Diphthamide is a modified histidine residue unique for eukaryotic translation elongation factor 2 (eEF2), a key ribosomal protein. Loss of this evolutionarily conserved modification causes developmental defects through unknown mechanisms. In a patient with compound heterozygous mutations in Diphthamide Biosynthesis 1 (DPH1) and impaired eEF2 diphthamide modification, we observe multiple defects in neural crest (NC)-derived tissues. Knockin mice harboring the patient’s mutations and Xenopus embryos with Dph1 depleted also display NC defects, which can be attributed to reduced proliferation in the neuroepithelium. DPH1 depletion facilitates dissociation of eEF2 from ribosomes and association with p53 to promote transcription of the cell cycle inhibitor p21, resulting in inhibited proliferation. Knockout of one p21 allele rescues the NC phenotypes in the knockin mice carrying the patient’s mutations. These findings uncover an unexpected role for eEF2 as a transcriptional coactivator for p53 to induce p21 expression and NC defects, which is regulated by diphthamide modification.

Abstract Decoding movement intentions from motor unit (MU) activities to represent neural drive information plays a central role in establishing neural interfaces, but there remains a great challenge for obtaining precise MU activities during sustained muscle contractions. In this paper, we presented an online muscle force prediction method driven by individual MU activities that were decomposed from prolonged surface electromyogram (SEMG) signals in real time. In the training stage of the proposed method, a set of separation vectors was initialized for decomposing MU activities. After transferring each decomposed MU activity into a twitch force train according to its action potential waveform, a neural network was designed and trained for predicting muscle force. In the subsequent online stage, a practical double-thread-parallel algorithm was developed. One frontend thread predicted the muscle force in real time utilizing the trained network and the other backend thread simultaneously updated the separation vectors. To assess the performance of the proposed method, SEMG signals were recorded from the abductor pollicis brevis muscles of eight subjects and the contraction force was simultaneously collected. With the update procedure in the backend thread, the force prediction performance of the proposed method was significantly improved in terms of lower root mean square deviation (RMSD) of around 10% and higher fitness (R2) of around 0.90, outperforming two conventional methods. This study provides a promising technique for real-time myoelectric applications in movement control and health.

Decoding movement intentions from motor unit (MU) activities remains an ongoing challenge, which restricts our comprehension of the intricate transition mechanism from microscopic neural drive to macroscopic movements. This study presents an innovative neuro-musculoskeletal (NMS) model driven by MU activities for online estimation of continuous wrist movements. The proposed model employs a physiological and comprehensive utilization of MU firings and waveforms, thus facilitating the localization of MUs to muscle-tendon units (MTU) as well as the computation of MU-specific neural excitation. Subsequently, the MU-specific neural excitation was integrated to form the MTU-specific neural excitation, which were then inputted into a musculoskeletal model to accomplish the joint angle estimation. To assess the effectiveness of this model, high-density surface electromyography and angular data were collected from the forearms of eight subjects during their performance of wrist flexion-extension task. Two pieces of $8\times 8$ electrode arrays and a motion capture system were employed for data acquisition. Following offline model calibration with a global optimization algorithm, online angle estimation results demonstrated a significant superiority of the proposed model over the state-of-the-art NMS models (p < 0.05), yielding the lowest normalized root mean square error ( $0.10~\pm ~0.02$ ) and the highest determination coefficient ( $0.87~\pm ~0.06$ ). This study provides a novel idea for the decoding of joint movements from MU activities. The research findings hold the potential to advance the development of NMS models towards the control of multiple degrees of freedom, with promising applications in the fields of motor control, biomechanics, and neuro-rehabilitation engineering.

The biosynthetic potential of actinobacteria to produce novel natural products is still regarded as immense. In this paper, we correlated a cryptic biosynthetic gene cluster to chemical molecules by genome mining and chemical analyses, leading to the discovery of a new group of catecholate-hydroxamate siderophores, nobachelins, from Nocardiopsis baichengensis DSM 44845. Nobachelin biosynthesis genes are conserved in several bacteria from the family Nocardiopsidaceae. Structurally, nobachelins feature fatty-acylated hydroxy-ornithine and a rare chlorinated catecholate group. Intriguingly, nobachelins rescued Caenorhabditis elegans from Pseudomonas aeruginosa-mediated killing.

An inverted ridge 3D thermal optical (TO) switch of a graphene-coated polymer/silica hybrid waveguide is proposed. The side electrode structure is designed to reduce the mode loss induced by the graphene film and by heating the electrode. The graphene layer is designed to be located on the waveguide to assist in the conduction of heat produced by the electrode. The inverted ridge core is fabricated by etching and spin-coating processes, which can realize the flat surface waveguide. This core improves the transfer of the graphene layer and the compatibility of the fabrication processes. Because of the opposite thermal optical coefficient of polymer and silica and the high thermal conductivity of the graphene layer, the 3D hybrid TO switch with low power consumption and fast response time is obtained. Compared with the traditional TO switch without graphene film, the power consumption of the proposed TO switch is reduced by 41.43% at the wavelength of 1550 nm, width of the core layer (a) of 3 μm, and electrode distance (d) of 4 μm. The rise and fall times of the proposed TO switch are simulated to be 64.5 μs and 175 μs with a d of 4 μm, and a of 2 μm, respectively.