159 research outputs found
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
Structural and biochemical insights into small RNA 3' end trimming by Arabidopsis SDN1.
A family of DEDDh 3'→5' exonucleases known as Small RNA Degrading Nucleases (SDNs) initiates the turnover of ARGONAUTE1 (AGO1)-bound microRNAs in Arabidopsis by trimming their 3' ends. Here, we report the crystal structure of Arabidopsis SDN1 (residues 2-300) in complex with a 9 nucleotide single-stranded RNA substrate, revealing that the DEDDh domain forms rigid interactions with the N-terminal domain and binds 4 nucleotides from the 3' end of the RNA via its catalytic pocket. Structural and biochemical results suggest that the SDN1 C-terminal domain adopts an RNA Recognition Motif (RRM) fold and is critical for substrate binding and enzymatic processivity of SDN1. In addition, SDN1 interacts with the AGO1 PAZ domain in an RNA-independent manner in vitro, enabling it to act on AGO1-bound microRNAs. These extensive structural and biochemical studies may shed light on a common 3' end trimming mechanism for 3'→5' exonucleases in the metabolism of small non-coding RNAs
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
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
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
Prognostic value of inflammatory nutritional scores in locally advanced esophageal squamous cell carcinoma patients undergoing neoadjuvant chemoimmunotherapy: a multicenter study in China
ObjectiveThis study investigates the prognostic significance of inflammatory nutritional scores in patients with locally advanced esophageal squamous cell carcinoma (LA-ESCC) undergoing neoadjuvant chemoimmunotherapy.MethodsA total of 190 LA-ESCC patients were recruited from three medical centers across China. Pre-treatment laboratory tests were utilized to calculate inflammatory nutritional scores. LASSO regression and multivariate logistic regression analyses were conducted to pinpoint predictors of pathological response. Kaplan-Meier and Cox regression analyses were employed to assess disease-free survival (DFS) prognostic factors.ResultsThe cohort comprised 154 males (81.05%) and 36 females (18.95%), with a median age of 61.4 years. Pathological complete response (pCR) was achieved in 17.38% of patients, while 44.78% attained major pathological response (MPR). LASSO and multivariate logistic regression analyses identified that hemoglobin, albumin, lymphocyte, and platelet (HALP) (P=0.02) as an independent predictors of MPR in LA-ESCC patients receiving neoadjuvant chemoimmunotherapy. Kaplan-Meier and log-rank tests indicated that patients with low HALP, MPR, ypT1-2, ypN0 and, ypTNM I stages had prolonged DFS (P < 0.05). Furthermore, univariate and multivariate Cox regression analyses underscored HALP (P = 0.019) and ypT (P = 0.029) as independent predictive factors for DFS in ESCC.ConclusionOur study suggests that LA-ESCC patients with lower pre-treatment HALP scores exhibit improved pathological response and reduced recurrence rate. As a comprehensive index of inflammatory nutritional status, pre-treatment HALP may be a reliable prognostic marker in ESCC patients undergoing neoadjuvant chemoimmunotherapy
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