Can Shaker Potassium Channels be Locked in the Deactivated State?

For structural studies it would be useful to constrain the voltage sensor of a voltage-gated channel in its deactivated state. Here we consider one Shaker potassium channel mutant and speculate about others that might allow the channel to remain deactivated at zero membrane potential. Ionic and gating currents of F370C Shaker, expressed in Xenopus oocytes, were recorded in patches with internal application of the methanethiosulfonate reagent MTSET. It appears that the voltage dependence of voltage sensor movement is strongly shifted by reaction with internal MTSET, such that the voltage sensors appear to remain deactivated even at positive potentials. A disadvantage of this construct is that the rate of modification of voltage sensors by MTSET is quite low, ∼0.17 mM−1·s−1 at −80 mV, and is expected to be much lower at depolarized potentials

A Two-Channel Patch-Clamp System on a Chip

The public emergency enactment, 1917. The quarantine and prevention of disease enactment, 1903

EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate

Mixture-of-experts (MoE) is becoming popular due to its success in improving the model quality, especially in Transformers. By routing tokens with a sparse gate to a few experts (i.e., a small pieces of the full model), MoE can easily increase the model parameters to a very large scale while keeping the computation cost in a constant level. Most existing works just initialize some random experts, set a fixed gating strategy (e.g., Top-k), and train the model from scratch in an ad-hoc way. We identify that these MoE models are suffering from the immature experts and unstable sparse gate, which are harmful to the convergence performance. In this paper, we propose an efficient end-to-end MoE training framework called EvoMoE. EvoMoE starts from training one single expert and gradually evolves into a large and sparse MoE structure. EvoMoE mainly contains two phases: the expert-diversify phase to train the base expert for a while and spawn multiple diverse experts from it, and the gate-sparsify phase to learn an adaptive sparse gate and activate a dynamic number of experts. EvoMoE naturally decouples the joint learning of both the experts and the sparse gate and focuses on learning the basic knowledge with a single expert at the early training stage. Then it diversifies the experts and continues to train the MoE with a novel Dense-to-Sparse gate (DTS-Gate). Specifically, instead of using a permanent sparse gate, DTS-Gate begins as a dense gate that routes tokens to all experts, then gradually and adaptively becomes sparser while routes to fewer experts. Evaluations are conducted on three popular models and tasks, including RoBERTa for masked language modeling task, GPT for language modeling task and Transformer for machine translation task. The results show that EvoMoE outperforms existing baselines, including Switch, BASE Layer, Hash Layer and StableMoE

SuperScaler: Supporting Flexible DNN Parallelization via a Unified Abstraction

With the growing model size, deep neural networks (DNN) are increasingly trained over massive GPU accelerators, which demands a proper parallelization plan that transforms a DNN model into fine-grained tasks and then schedules them to GPUs for execution. Due to the large search space, the contemporary parallelization plan generators often rely on empirical rules that couple transformation and scheduling, and fall short in exploring more flexible schedules that yield better memory usage and compute efficiency. This tension can be exacerbated by the emerging models with increasing complexity in their structure and model size. SuperScaler is a system that facilitates the design and generation of highly flexible parallelization plans. It formulates the plan design and generation into three sequential phases explicitly: model transformation, space-time scheduling, and data dependency preserving. Such a principled approach decouples multiple seemingly intertwined factors and enables the composition of highly flexible parallelization plans. As a result, SuperScaler can not only generate empirical parallelization plans, but also construct new plans that achieve up to 3.5X speedup compared to state-of-the-art solutions like DeepSpeed, Megatron and Alpa, for emerging DNN models like Swin-Transformer and AlphaFold2, as well as well-optimized models like GPT-3

Research on bearing capacity of cross-type truss boom with variable cross-section of Crawler cranes

The web crossed truss boom is one of the commonly used truss boom structures of crawler cranes. However, the existing calculations fail to consider the limiting effect of the web members' bending resistance on the chord members, and cannot give full play to the load-bearing capacity of the existing structure. This paper takes the top section of the Crawler crane truss boom as the research object. The single-span truss theoretical model is established according to Timoshenko's elastic stability theory. And the theoretical critical load of the variable cross-section boom is obtained with full consideration of the limitation of the web member's bending resistance on the chord members. The finite element method simulation model is compared and verified. Compared with a large number of simulation experiments and theoretical calculations, it can be concluded that the theoretical calculations in this article are highly consistent with the simulation results, verified the assumptions that the web members' bending resistance help to improve the bending resistance of the chord members, and this will provide certain reference to the engineering designers

Asp433 in the closing gate of ASIC1 determines stability of the open state without changing properties of the selectivity filter or Ca2+ block

A constriction formed by the crossing of the second transmembrane domains of ASIC1, residues G432 to G436, forms the narrowest segment of the pore in the crystal structure of chicken ASIC1, presumably in the desensitized state, suggesting that it constitutes the “desensitization gate” and the “selectivity filter.” Residues Gly-432 and Asp-433 occlude the pore, preventing the passage of ions from the extracellular side. Here, we examined the role of Asp-433 and Gly-432 in channel kinetics, ion selectivity, conductance, and Ca2+ block in lamprey ASIC1 that is a channel with little intrinsic desensitization in the pH range of maximal activity, pH 7.0. The results show that the duration of open times depends on residue 433, with Asp supporting the longest openings followed by Glu, Gln, or Asn, whereas other residues keep the channel closed. This is consistent with residue Asp-433 forming the pore’s closing gate and the properties of the side chain either stabilizing (hydrophobic amino acids) or destabilizing (Asp) the gate. The data also show residue 432 influencing the duration of openings, but here only Gly and Ala support long openings, whereas all other residues keep channels closed. The negative charge of Asp-433 was not required for block of the open pore by Ca2+ or for determining ion selectivity and unitary conductance. We conclude that the conserved residue Asp-433 forms the closing gate of the pore and thereby determines the duration of individual openings while desensitization, defined as the permanent closure of all or a fraction of channels by the continual presence of H+, modulates the on or off position of the closing gate. The latter effect depends on less conserved regions of the channel, such as TM1 and the extracellular domain. The constriction made by Asp-433 and Gly-432 does not select for ions in the open conformation, implying that the closing gate and selectivity filter are separate structural elements in the ion pathway of ASIC1. The results also predict a significantly different conformation of TM2 in the open state that relieves the constriction made by TM2, allowing the passage of ions unimpeded by the side chain of Asp-433