Constructing Physical and Genomic Maps for Puccinia striiformis f. sp. tritici, the Wheat Stripe Rust Pathogen, by Comparing Its EST Sequences to the Genomic Sequence of P. graminis f. sp. tritici, the Wheat Stem Rust Pathogen

The wheat stripe rust fungus, Puccinia striiformis f. sp. tritici (Pst), does not have a known alternate host for sexual reproduction, which makes it impossible to study gene linkages through classic genetic and molecular mapping approaches. In this study, we compared 4,219 Pst expression sequence tags (ESTs) to the genomic sequence of P. graminis f. sp. tritici (Pgt), the wheat stem rust fungus, using BLAST searches. The percentages of homologous genes varied greatly among different Pst libraries with 54.51%, 51.21%, and 13.61% for the urediniospore, germinated urediniospore, and haustorial libraries, respectively, with an average of 33.92%. The 1,432 Pst genes with significant homology with Pgt sequences were grouped into physical groups corresponding to 237 Pgt supercontigs. The physical relationship was demonstrated by 12 pairs (57%), out of 21 selected Pst gene pairs, through PCR screening of a Pst BAC library. The results indicate that the Pgt genome sequence is useful in constructing Pst physical maps

Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization

Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are still struggling to achieve high learning efficiency and solution quality. To tackle this issue, we propose an efficient meta neural heuristic (EMNH), in which a meta-model is first trained and then fine-tuned with a few steps to solve corresponding single-objective subproblems. Specifically, for the training process, a (partial) architecture-shared multi-task model is leveraged to achieve parallel learning for the meta-model, so as to speed up the training; meanwhile, a scaled symmetric sampling method with respect to the weight vectors is designed to stabilize the training. For the fine-tuning process, an efficient hierarchical method is proposed to systematically tackle all the subproblems. Experimental results on the multi-objective traveling salesman problem (MOTSP), multi-objective capacitated vehicle routing problem (MOCVRP), and multi-objective knapsack problem (MOKP) show that, EMNH is able to outperform the state-of-the-art neural heuristics in terms of solution quality and learning efficiency, and yield competitive solutions to the strong traditional heuristics while consuming much shorter time.Comment: Accepted at NeurIPS 202

Model Construction and Numerical Simulation for Hydroplaning of Complex Tread Tires

Euler-Lagrange coupling method is used to establish the fluid-structure interaction model for tires with different tread patterns by obtaining the grounding mark and normal contact force between tire and the road surface during tire rolling. The altering of load force, tire pressure, and water film thickness in relation to the effect on tire-road force during both constant speed and critical hydroplaning speed was analyzed. Results show that the critical hydroplaning speed and normal contact force between tire and the road surface are positively correlated with vehicle load and tire pressure and negatively correlated with water film thickness. Python language is used to develop the pre-processing plug-ins to achieve parametric modeling and rapid creation of Finite Element Analysis (FEA) model to reduce time costs, and the effectiveness of the plug-ins is verified through comparative tests

Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement

Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set. Beyond decomposition, we propose a novel neural heuristic with diversity enhancement (NHDE) to produce more Pareto solutions from two perspectives. On the one hand, to hinder duplicated solutions for different subproblems, we propose an indicator-enhanced deep reinforcement learning method to guide the model, and design a heterogeneous graph attention mechanism to capture the relations between the instance graph and the Pareto front graph. On the other hand, to excavate more solutions in the neighborhood of each subproblem, we present a multiple Pareto optima strategy to sample and preserve desirable solutions. Experimental results on classic MOCO problems show that our NHDE is able to generate a Pareto front with higher diversity, thereby achieving superior overall performance. Moreover, our NHDE is generic and can be applied to different neural methods for MOCO.Comment: Accepted at NeurIPS 202

PsRPs26, a 40S Ribosomal Protein Subunit, Regulates the Growth and Pathogenicity of Puccinia striiformis f. sp. Tritici

Eukaryotic ribosomes are essential for proliferation, differentiation, and cell growth. RPs26 is a ribosomal subunit structural protein involved in the growth and development process. Little is known about the function of PsRPs26 in pathogenic fungi. In this study, we isolated the RPs26 gene, PsRPs26, from Puccinia striiformis f. sp. tritici (Pst). PsRPs26 contains a eukaryotic-specific Y62–K70 motif and is more than 90% identical with its ortholog gene in other fungi. PsRPs26 was found to be localized in both the nucleus and cytoplasm. Expression of PsRPs26 increased when wheat seedlings were inoculated with the Pst CYR31 isolate. Moreover, knockdown of PsRPs26 by a host-induced gene silencing system inhibited growth and limited urediospore production in Pst. Our discovery that PsRPs26 may contribute to the pathogenicity of Pst and open a new way in the pathogenic function of PsRPs26 in cereal rust fungi

Structural insights into molecular mechanism for N6-adenosine methylation by MT-A70 family methyltransferase METTL4

METTL4 belongs to a subclade of MT-A70 family members of methyltransferase (MTase) proteins shown to mediate N6-adenosine methylation for both RNA and DNA in diverse eukaryotes. Here, we report that Arabidopsis METTL4 functions as U2 snRNA MTase for N6−2’-O-dimethyladenosine (m6Am) in vivo that regulates flowering time, and specifically catalyzes N6-methylation of 2’-O-methyladenosine (Am) within a single-stranded RNA in vitro. The apo structures of full-length Arabidopsis METTL4 bound to S-adenosyl-L-methionine (SAM) and the complex structure with an Am-containing RNA substrate, combined with mutagenesis and in vitro enzymatic assays, uncover a preformed L-shaped, positively-charged cavity surrounded by four loops for substrate binding and a catalytic center composed of conserved residues for specific Am nucleotide recognition and N6-methylation activity. Structural comparison of METTL4 with the mRNA m6A enzyme METTL3/METTL14 heterodimer and modeling analysis suggest a catalytic mechanism for N6-adenosine methylation by METTL4, which may be shared among MT-A70 family members

The Chinese Open Science Network (COSN): Building an Open Science Community From Scratch

Open Science is becoming a mainstream scientific ideology in psychology and related fields. However, researchers, especially early-career researchers (ECRs) in developing countries, are facing significant hurdles in engaging in Open Science and moving it forward. In China, various societal and cultural factors discourage ECRs from participating in Open Science, such as the lack of dedicated communication channels and the norm of modesty. To make the voice of Open Science heard by Chinese-speaking ECRs and scholars at large, the Chinese Open Science Network (COSN) was initiated in 2016. With its core values being grassroots-oriented, diversity, and inclusivity, COSN has grown from a small Open Science interest group to a recognized network both in the Chinese-speaking research community and the international Open Science community. So far, COSN has organized three in-person workshops, 12 tutorials, 48 talks, and 55 journal club sessions and translated 15 Open Science-related articles and blogs from English to Chinese. Currently, the main social media account of COSN (i.e., the WeChat Official Account) has more than 23,000 subscribers, and more than 1,000 researchers/students actively participate in the discussions on Open Science. In this article, we share our experience in building such a network to encourage ECRs in developing countries to start their own Open Science initiatives and engage in the global Open Science movement. We foresee great collaborative efforts of COSN together with all other local and international networks to further accelerate the Open Science movement

Identification of Puccinia striiformis races from the spring wheat crop in Xinjiang, China

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a foliar disease that affects both winter and spring wheat crops in Xinjiang, China, which is linked to Central Asia. Race identification of Pst from spring wheat in Xinjiang was not done before. In this study, a total of 216 isolates were recovered from stripe rust samples of spring wheat in the region in 2021 and multiplied using the susceptible cultivar Mingxian 169. These isolates were tested on the Chinese set of 19 wheat differential lines for identifying Pst races. A total of 46 races were identified. Races Suwon-11-1, Suwon11-12, and CYR32 had high frequencies in the spring wheat region. The frequencies of virulence factors on differentials “Fulhard” and “Early Premium” were high (>95%), whereas the virulence factor to differential “Triticum spelta var. Album” (Yr5) was not detected, while virulence to other differentials showed variable frequency within different counties. The predominant races in winter wheat in the same season were also detected from spring wheat cultivars, indicating Pst spreading from winter wheat to spring wheat crops. Deploying resistance genes in spring and winter wheat cultivars is critical for control stripe rust

Undoped Strained Ge Quantum Well with Ultrahigh Mobility Grown by Reduce Pressure Chemical Vapor Deposition

We fabricate an undoped Ge quantum well under 30 nm Ge0.8Si0.2 shallow barrier with reverse grading technology. The under barrier is deposited by Ge0.8Si0.2 followed by Ge0.9Si0.1 so that the variation of Ge content forms a sharp interface which can suppress the threading dislocation density penetrating into undoped Ge quantum well. And the Ge0.8Si0.2 barrier introduces enough in-plane parallel strain -0.41% in the Ge quantum well. The heterostructure field-effect transistors with a shallow buried channel get a high two-dimensional hole gas (2DHG) mobility over 2E6 cm2/Vs at a low percolation density of 2.51 E-11 cm2. We also discover a tunable fractional quantum Hall effect at high densities and high magnetic fields. This approach defines strained germanium as providing the material basis for tuning the spin-orbit coupling strength for fast and coherent quantum computation.Comment: 11 pages, 5 figure