Magnetosome Gene Duplication as an Important Driver in the Evolution of Magnetotaxis in the Alphaproteobacteria

The evolution of microbial magnetoreception (or magnetotaxis) is of great interest in the fields of microbiology, evolutionary biology, biophysics, geomicrobiology, and geochemistry. Current genomic data from magnetotactic bacteria (MTB), the only prokaryotes known to be capable of sensing the Earth’s geomagnetic field, suggests an ancient origin of magnetotaxis in the domain Bacteria. Vertical inheritance, followed by multiple independent magnetosome gene cluster loss, is considered to be one of the major forces that drove the evolution of magnetotaxis at or above the class or phylum level, although the evolutionary trajectories at lower taxonomic ranks (e.g., within the class level) remain largely unstudied. Here we report the isolation, cultivation, and sequencing of a novel magnetotactic spirillum belonging to the genus Terasakiella (Terasakiella sp. strain SH-1) within the class Alphaproteobacteria. The complete genome sequence of Terasakiella sp. strain SH-1 revealed an unexpected duplication event of magnetosome genes within the mamAB operon, a group of genes essential for magnetosome biomineralization and magnetotaxis. Intriguingly, further comparative genomic analysis suggests that the duplication of mamAB genes is a common feature in the genomes of alphaproteobacterial MTB. Taken together, with the additional finding that gene duplication appears to have also occurred in some magnetotactic members of the Deltaproteobacteria, our results indicate that gene duplication plays an important role in the evolution of magnetotaxis in the Alphaproteobacteria and perhaps the domain Bacteria

Motion control and path optimization of intelligent AUV using fuzzy adaptive PID and improved genetic algorithm

This study discusses the application of fuzzy adaptive PID and improved genetic algorithm (IGA) in motion control and path optimization of autonomous underwater vehicle (AUV). The fuzzy adaptive PID method is selected because it is considered to be a strongly nonlinear and coupled system. First, this study creates the basic coordinate system of the AUV, and then analyzes the spatial force from the AUV to obtain the control model of the heading angle, climb angle, and depth. Next, the knowledge of fuzzy adaptive PID and IGA technology on AVU are investigated, then fuzzy adaptive PID controllers and path optimization are established, and experimental simulations are carried out to compare and analyze the simulation results. The research results show that controllers and IGA can be used for the motion control and path optimization of AUV. The advantages of fuzzy adaptive PID control are less overload, enhanced system stability, and more suitable for motion control and path optimization of AUV

Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition

Low-resource automatic speech recognition (ASR) is challenging, as the low-resource target language data cannot well train an ASR model. To solve this issue, meta-learning formulates ASR for each source language into many small ASR tasks and meta-learns a model initialization on all tasks from different source languages to access fast adaptation on unseen target languages. However, for different source languages, the quantity and difficulty vary greatly because of their different data scales and diverse phonological systems, which leads to task-quantity and task-difficulty imbalance issues and thus a failure of multilingual meta-learning ASR (MML-ASR). In this work, we solve this problem by developing a novel adversarial meta sampling (AMS) approach to improve MML-ASR. When sampling tasks in MML-ASR, AMS adaptively determines the task sampling probability for each source language. Specifically, for each source language, if the query loss is large, it means that its tasks are not well sampled to train ASR model in terms of its quantity and difficulty and thus should be sampled more frequently for extra learning. Inspired by this fact, we feed the historical task query loss of all source language domain into a network to learn a task sampling policy for adversarially increasing the current query loss of MML-ASR. Thus, the learnt task sampling policy can master the learning situation of each language and thus predicts good task sampling probability for each language for more effective learning. Finally, experiment results on two multilingual datasets show significant performance improvement when applying our AMS on MML-ASR, and also demonstrate the applicability of AMS to other low-resource speech tasks and transfer learning ASR approaches.Comment: accepted in AAAI202

Dual Averaging Method for Online Graph-structured Sparsity

Online learning algorithms update models via one sample per iteration, thus efficient to process large-scale datasets and useful to detect malicious events for social benefits, such as disease outbreak and traffic congestion on the fly. However, existing algorithms for graph-structured models focused on the offline setting and the least square loss, incapable for online setting, while methods designed for online setting cannot be directly applied to the problem of complex (usually non-convex) graph-structured sparsity model. To address these limitations, in this paper we propose a new algorithm for graph-structured sparsity constraint problems under online setting, which we call \textsc{GraphDA}. The key part in \textsc{GraphDA} is to project both averaging gradient (in dual space) and primal variables (in primal space) onto lower dimensional subspaces, thus capturing the graph-structured sparsity effectively. Furthermore, the objective functions assumed here are generally convex so as to handle different losses for online learning settings. To the best of our knowledge, \textsc{GraphDA} is the first online learning algorithm for graph-structure constrained optimization problems. To validate our method, we conduct extensive experiments on both benchmark graph and real-world graph datasets. Our experiment results show that, compared to other baseline methods, \textsc{GraphDA} not only improves classification performance, but also successfully captures graph-structured features more effectively, hence stronger interpretability.Comment: 11 pages, 14 figure

LC/MS Guided Isolation of Alkaloids from Lotus Leaves by pH-Zone-Refining Counter-Current Chromatography

The traditional methods used in natural product separation primarily target the major components and the minor components may thus be lost during the separation procedure. Consequently, it’s necessary to develop efficient methods for the preparative separation and purification of relatively minor bioactive components. In this paper, a LC/MS method was applied to guide the separation of crude extract of lotus (Nelumbo nucifera Gaertn.) leaves whereby a minor component was identified in the LC/MS analysis. Afterwards, an optimized pH-zone-refining CCC method was performed to isolate this product, identified as N-demethylarmepavine. The separation procedure was carried out with a biphasic solvent system composed of hexane-ethyl acetate-methyl alcohol-water (1:6:1:6, v/v) with triethylamine (10 mM) added to the upper organic phase as a retainer and hydrochloric acid (5 mM) to the aqueous mobile phase eluent. Two structurally similar compounds – nuciferine and roemerine – were also obtained from the crude lotus leaves extract. In total 500 mg of crude extract furnished 7.4 mg of N-demethylarmepavine, 45.3 mg of nuciferine and 26.6 mg of roemerine with purities of 90%, 92% and 96%, respectively. Their structures were further identified by HPLC/ESI-MSn, FTICR/MS and the comparison with reference compounds

Recent changes to Arctic river discharge

Arctic rivers drain ~15% of the global land surface and significantly influence local communities and economies, freshwater and marine ecosystems, and global climate. However, trusted and public knowledge of pan-Arctic rivers is inadequate, especially for small rivers and across Eurasia, inhibiting understanding of the Arctic response to climate change. Here, we calculate daily streamflow in 486,493 pan-Arctic river reaches from 1984-2018 by assimilating 9.18 million river discharge estimates made from 155,710 satellite images into hydrologic model simulations. We reveal larger and more heterogenous total water export (3-17% greater) and water export acceleration (factor of 1.2-3.3 larger) than previously reported, with substantial differences across basins, ecoregions, stream orders, human regulation, and permafrost regimes. We also find significant changes in the spring freshet and summer stream intermittency. Ultimately, our results represent an updated, publicly available, and more accurate daily understanding of Arctic rivers uniquely enabled by recent advances in hydrologic modeling and remote sensing