Mapping the mammalian ribosome quality control complex interactome using proximity labeling approaches.

Previous genetic and biochemical studies from Saccharomyces cerevisiae have identified a critical ribosome-associated quality control complex (RQC) that facilitates resolution of stalled ribosomal complexes. While components of the mammalian RQC have been examined in vitro, a systematic characterization of RQC protein interactions in mammalian cells has yet to be described. Here we utilize both proximity-labeling proteomic approaches, BioID and APEX, and traditional affinity-based strategies to both identify interacting proteins of mammalian RQC members and putative substrates for the RQC resident E3 ligase, Ltn1. Surprisingly, validation studies revealed that a subset of substrates are ubiquitylated by Ltn1 in a regulatory manner that does not result in subsequent substrate degradation. We demonstrate that Ltn1 catalyzes the regulatory ubiquitylation of ribosomal protein S6 kinase 1 and 2 (RPS6KA1, RPS6KA3). Further, loss of Ltn1 function results in hyperactivation of RSK1/2 signaling without impacting RSK1/2 protein turnover. These results suggest that Ltn1-mediated RSK1/2 ubiquitylation is inhibitory and establishes a new role for Ltn1 in regulating mitogen-activated kinase signaling via regulatory RSK1/2 ubiquitylation. Taken together, our results suggest that mammalian RQC interactions are difficult to observe and may be more transient than the homologous complex in S. cerevisiae and that Ltn1 has RQC-independent functions

Flow-Dependent Myosin Recruitment during Drosophila Cellularization Requires Zygotic Dunk Activity

Actomyosin contractility underlies force generation in morphogenesis ranging from cytokinesis to epithelial extension or invagination. In Drosophila, the cleavage of the syncytial blastoderm is initiated by an actomyosin network at the base of membrane furrows that invaginate from the surface of the embryo. It remains unclear how this network forms and how it affects tissue mechanics. Here, we show that during Drosophila cleavage, myosin recruitment to the cleavage furrows proceeds in temporally distinct phases of tension-driven cortical flow and direct recruitment, regulated by different zygotic genes. We identify the gene dunk, which we show is transiently transcribed when cellularization starts and functions to maintain cortical myosin during the flow phase. The subsequent direct myosin recruitment, however, is Dunk-independent but requires Slam. The Slam-dependent direct recruitment of myosin is sufficient to drive cleavage in the dunk mutant, and the subsequent development of the mutant is normal. In the dunk mutant, cortical myosin loss triggers misdirected flow and disrupts the hexagonal packing of the ingressing furrows. Computer simulation coupled with laser ablation suggests that Dunk-dependent maintenance of cortical myosin enables mechanical tension build-up, thereby providing a mechanism to guide myosin flow and define the hexagonal symmetry of the furrows

Loss-tolerant quantum secure positioning with weak laser sources

Quantum position verification (QPV) is the art of verifying the geographical location of an untrusted party. Recently, it has been shown that the widely studied Bennett & Brassard 1984 (BB84) QPV protocol is insecure after the 3 dB loss point assuming local operations and classical communication (LOCC) adversaries. Here, we propose a time-reversed entanglement swapping QPV protocol (based on measurement-device-independent quantum cryptography) that is highly robust against quantum channel loss. First, assuming ideal qubit sources, we show that the protocol is secure against LOCC adversaries for any quantum channel loss, thereby overcoming the 3 dB loss limit. Then, we analyze the security of the protocol in a more practical setting involving weak laser sources and linear optics. In this setting, we find that the security only degrades by an additive constant and the protocol is able to verify positions up to 47 dB channel loss.Comment: 11 pages, 3 figures. Partially based on an earlier work in arXiv:1510.0489

Adaptive Admittance Control Strategy for a Robotic Knee Exoskeleton With a Nonlinear Variable Stiffness Actuator

This article presents the design and control of a robotic knee exoskeleton for gait rehabilitation of patients with knee joint impairments. First, the hardware design of the exoskeleton is presented, including the mechanical structure, actuator design and configuration, and electronic system. Based on the nonlinear characteristics of human muscles, a nonlinear variable stiffness actuator (NLVSA) is designed for the actuation system of the exoskeleton. Next, the modeling of the NLVSA is described. In addition, an adaptive admittance control strategy comprising a sparrow search optimization algorithm-based long short-term memory neural network model and an adaptive admittance control algorithm based on the radial basis function neural network (RBFAAC) is proposed for the exoskeleton. Finally, a pilot study is conducted to demonstrate the effectiveness of the robotic knee exoskeleton. The experimental results validate the effectiveness of the designed NLVSA, and the exoskeleton has the potential for human knee rehabilitation by providing effective assistance with the proposed control strategy. With the proposed RBFAAC algorithm, the average root mean square error between the reference and actual knee joint angles is 1.24° at different walking speeds

Bilingual Word Spectral Clustering for Statistical Machine Translation

In this paper, a variant of a spectral clustering algorithm is proposed for bilingual word clustering. The proposed algorithm generates the two sets of clusters for both languages efficiently with high semantic correlation within monolingual clusters, and high translation quality across the clusters between two languages. Each cluster level translation is considered as a bilingual concept, which generalizes words in bilingual clusters. This scheme improves the robustness for statistical machine translation models. Two HMM-based translation models are tested to use these bilingual clusters. Improved perplexity, word alignment accuracy, and translation quality are observed in our experiments

Delayed self-feedback echo state network for long-term dynamics of hyperchaotic systems

Analyzing the long-term behavior of hyperchaotic systems poses formidable challenges in the field of nonlinear science. This paper proposes a data-driven model called the delayed self-feedback echo state network (self-ESN) specifically designed for the evolution behavior of hyperchaotic systems. Self-ESN incorporates a delayed self-feedback term into the dynamic equation of a reservoir to reflect the finite transmission speed of neuron signals. Delayed self-feedback establishes a connection between the current and previous time steps of the reservoir state and provides an effective means to capture the dynamic characteristics of the system, thereby significantly improving memory performance. In addition, the concept of local echo state property (ESP) is introduced to relax the conventional ESP condition, and theoretical analysis is conducted on guiding the selection of feedback delay and gain to ensure the local ESP. The judicious selection of feedback gain and delay in self-ESN improves prediction accuracy and overcomes the challenges associated with obtaining optimal parameters of the reservoir in conventional ESN models. Numerical experiments are conducted to assess the long-term prediction capabilities of the self-ESN across various scenarios, including a 4D hyperchaotic system, a hyperchaotic network, and an infinite-dimensional delayed chaotic system. The experiments involve reconstructing bifurcation diagrams, predicting the chaotic synchronization, examining spatiotemporal evolution patterns, and uncovering the hidden attractors. The results underscore the capability of the proposed self-ESN as a strategy for long-term prediction and analysis of the complex systems

Delayed self-feedback echo state network for long-term dynamics of hyperchaotic systems

Analyzing the long-term behavior of hyperchaotic systems poses formidable challenges in the field of nonlinear science. This paper proposes a data-driven model called the delayed self-feedback echo state network (self-ESN) specifically designed for the evolution behavior of hyperchaotic systems. Self-ESN incorporates a delayed self-feedback term into the dynamic equation of a reservoir to reflect the finite transmission speed of neuron signals. Delayed self-feedback establishes a connection between the current and previous time steps of the reservoir state and provides an effective means to capture the dynamic characteristics of the system, thereby significantly improving memory performance. In addition, the concept of local echo state property (ESP) is introduced to relax the conventional ESP condition, and theoretical analysis is conducted on guiding the selection of feedback delay and gain to ensure the local ESP. The judicious selection of feedback gain and delay in self-ESN improves prediction accuracy and overcomes the challenges associated with obtaining optimal parameters of the reservoir in conventional ESN models. Numerical experiments are conducted to assess the long-term prediction capabilities of the self-ESN across various scenarios, including a 4D hyperchaotic system, a hyperchaotic network, and an infinite-dimensional delayed chaotic system. The experiments involve reconstructing bifurcation diagrams, predicting the chaotic synchronization, examining spatiotemporal evolution patterns, and uncovering the hidden attractors. The results underscore the capability of the proposed self-ESN as a strategy for long-term prediction and analysis of the complex systems