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Cooking quality is the main factor determining the market value of rice. Although several major genes and a certain number of QTLs controlling cooking quality have been identified, the genetic complexity and environmental susceptibility limit the further improvement for cooking quality by molecular breeding. This research conducted a genome-wide association study to elucidate the QTLs related to cooking quality including amylose content (AC), gel consistency (GC) and alkali spreading value (ASV) by using 450 rice accessions consisting of 300 indica and 150 japonica accessions in two distinct environments. A total of 54 QTLs were identified, including 25 QTLs for AC, 12 QTLs for GC and 17 QTLs for ASV. Among them, 10 QTLs were consistently observed by the same population in both environments. Six QTLs were co-localized with the reported QTLs or cloned genes. The Wx gene for AC and GC, and the ALK gene for ASV were identified in every population across the two environments. The qAC9-2 for AC and the qGC9-2 for GC were defined to the same interval. The OsRING315 gene, encoding an E3 ubiquitin ligase, was considered as the candidate gene for both qAC9-2 and qGC9-2. The higher expression of OsRING315 corresponded to the lower AC and higher GC. Three haplotypes of OsRING315 were identified. The Hap 1 mainly existed in the japonica accessions and had lower AC. The Hap 2 and Hap 3 were predominantly present in the indica accessions, associated with higher AC. Meanwhile, the GC of accessions harboring Hap 1 was higher than that of accessions harboring Hap 3. In addition, the distribution of the three haplotypes in several rice-growing regions was unbalanced. The three traits of cooking quality are controlled by both major and minor genes and susceptible to environmental factors. The expression level of OsRING315 is related to both AC and GC, and this gene can be a promising target in quality improvement by using the gene editing method. Moreover, the haplotypes of OsRING315 differentiate between indica and japonica, and reveal the differences in GC and AC between indica and japonica rice. [ABSTRACT FROM AUTHOR]

Transformer is widely used in natural language processing (NLP) tasks due to the parallel and modeling of long texts. However, its performance in Chinese named entity recognition (NER) is not effective. While distance, direction, and information on global and local perspectives of sequence are all important for NER tasks, the traditional transformer structure only focus on distance and partial global information by fully connected self-attention mechanism. In this paper, we propose a multi-view and sliding attention (MVSA) model to enhance transformer's ability to model Chinese character-word features in NER task. MVSA combines directional information to extract character-word features from multiple views, proposes a weighted ternary fusion method for feature fusion and uses slider attention mechanisms to enhance the local representation ability of the model. Experiments on five Chinese NER datasets show that MVSA achieves superior performance than CNN-based, LSTM-based and traditional transformer-based models. [ABSTRACT FROM AUTHOR]

Cultivating rice varieties with robust blast resistance is the most effective and economical way to manage the rice blast disease. However, rice blast disease comprises leaf and panicle blast, which are different in terms of resistance mechanisms. While many blast resistant rice cultivars were bred using genes conferring resistance to only leaf or panicle blast, mining durable and effective quantitative trait loci (QTLs) for both panicle and leaf blast resistance is of paramount importance. In this study, we conducted a pangenome-wide association study (panGWAS) on 9 blast resistance related phenotypes using 414 international diverse rice accessions from an international rice panel. This approach led to the identification of 74 QTLs associated with rice blast resistance. One notable locus, qPBR1, validated in a F4:5 population and fine-mapped in a Heterogeneous Inbred Family (HIF), exhibited broad-spectrum, major and durable blast resistance throughout the growth period. Furthermore, we performed transcriptomic analysis of 3 resistant and 3 sensitive accessions at different time points after infection, revealing 3,311 differentially expressed genes (DEGs) potentially involved in blast resistance. Integration of the above results identified 6 candidate genes within the qPBR1 locus, with no significant negative effect on yield. The results of this study provide valuable germplasm resources, QTLs, blast response genes and candidate functional genes for developing rice varieties with enduring and broad-spectrum blast resistance. The qPBR1, in particular, holds significant potential for breeding new rice varieties with comprehensive and durable resistance throughout their growth period. [ABSTRACT FROM AUTHOR]

Strong seedling vigor is imperative to achieve stable seedling establishment and enhance the competitiveness against weeds in rice direct seeding. Shoot length (SL) is one of the important traits associated with seedling vigor in rice, but few genes for SL have been cloned so far. In the previous study, we identified two tightly linked and stably expressed QTLs for SL, qSL-1f and qSL-1d by genome-wide association study, and cloned the causal gene (LOC_Os01g68500) underlying qSL-1f. In the present study, we identify LOC_Os01g66100 (i.e. the semidwarf gene SD1), a well-known gene controlling plant height (PH) at the adult-plant stage, as the causal gene underlying qSL-1d through gene-based haplotype analysis and knockout transgenic verification. By measuring the phenotypes (SL and PH) of various haplotypes of the two genes and their knockout lines, we found SD1 and LOC_ Os01g68500 controlled both SL and PH, and worked in the same direction, which provided the directly genetic evidence for a positive correlation between SL and PH combined with the analysis of SL and PH in the diverse natural population. Moreover, the knockout transgenic experiments suggested that SD1 had a greater effect on PH compared with LOC_ Os01g68500, but no significant difference in the effect on SL. Further investigation of the pyramiding effects of SD1 and LOC_Os01g68500 based on their haplotype combinations suggested that SD1 may play a dominant role in controlling SL and PH when the two genes coexist. In this study, the effect of SD1 on SL at the seedling stage is validated. In total, two causal genes, SD1 and LOC_ Os01g68500, for SL are cloned in our studies, which controlled both SL and PH, and the suitable haplotypes of SD1 and LOC_ Os01g68500 are beneficial to achieve the desired SL and PH in different rice breeding objectives. These results provide a new clue to develop rice varieties for direct seeding and provide new genetic resources for molecular breeding of rice with suitable PH and strong seedling vigor. [ABSTRACT FROM AUTHOR]

Social network rumor harm metric is a task to score the harm caused by a rumor by analyzing the spreading range of the rumor, the users affected, the repercussions caused, etc., and then the harm caused by the rumor. Rumor hazard metric models can help rumor detection digital twins to understand and analyze user behaviors and assist social network network managers to make more informed decisions. However, there is a lack of models that can quantify the harm of rumors and automated harm metric models in rumor detection digital twins. To address this issue, this paper proposes an innovative social network rumor harm metric based on rumor propagation knowledge and a large language model (LLM), RSK-T5. The method first completes the joint task of rumor comment stance detection and sentiment analysis to capture critical features of rumor propagation. Then, this knowledge is used in the pre-training process of LLM to improve the model's understanding of rumor propagation patterns. Finally, the fine-tuning phase focuses on the hazard metrics task to improve the generalization energy. We compare with some existing variants of rumor detection methods, and experimental results demonstrate that RSK-T5 achieves the lowest MSE scores on three well-known rumor detection datasets. The ablative learning work demonstrates the effectiveness of RSK-T5's knowledge of two rumor spreads. [ABSTRACT FROM AUTHOR]

This article explores the chemical constituents of Salvia cavaleriei var. simplicifolia, a plant used for medicinal purposes by the Miao and Dong nations in Guizhou Province, China. The researchers isolated seven compounds from the plant, including dehydrodiisoeugenol and 14-formyl-2,7-dihydroxycadalene, which were isolated from the Salvia genus for the first time. The article provides detailed information about the extraction and purification processes used to obtain these compounds. The compounds were analyzed using nuclear magnetic resonance spectroscopy, and their structures and characteristics were determined. Some of the compounds showed potential hemostatic activity. The research was supported by various funding sources. [Extracted from the article]

Aims: Human activities have more than doubled the input of atmospheric reactive nitrogen into terrestrial ecosystems, affecting plant and soil microbial communities. This study aimed to investigate the effect of atmospheric N deposition on the rhizosphere soil microbial community composition of the main plant species with different niches in grassland.An N addition experiment was performed in a semi-arid grassland on the Loess Plateau, China. The experiment consisted of four N addition treatments: N0 (0 g N m−2 yr−1), N3 (3 g N m−2 yr−1), N6 (6 g N m−2 yr−1), and N9 (9 g N m−2 yr−1). The response of rhizosphere soil microbial community composition from four main plant species to atmospheric N deposition was explored.Nitrogen addition significantly affected rhizosphere soil bacterial community composition of Bothriochloa ischaemum and Stipa bungeana but had no effect on that of Artemisia gmelinii and Cleistogenes squarrosa. Furthermore, it also significantly affected rhizosphere soil fungal community composition of S.bungeana; however, no effects were observed on the other three plant species. Compared with the complexity of rhizosphere soil bacterial network, low N addition rates had a greater effect on the complexity of the rhizosphere soil fungal network.This study revealed that rhizosphere soil bacterial community composition of plant species with a higher niche breadth is generally more sensitive to atmospheric N deposition. Our study highlights the distinct roles of plant species with different niche breadths in maintaining rhizosphere soil microbial community composition under conditions of global N enrichment.Methods: Human activities have more than doubled the input of atmospheric reactive nitrogen into terrestrial ecosystems, affecting plant and soil microbial communities. This study aimed to investigate the effect of atmospheric N deposition on the rhizosphere soil microbial community composition of the main plant species with different niches in grassland.An N addition experiment was performed in a semi-arid grassland on the Loess Plateau, China. The experiment consisted of four N addition treatments: N0 (0 g N m−2 yr−1), N3 (3 g N m−2 yr−1), N6 (6 g N m−2 yr−1), and N9 (9 g N m−2 yr−1). The response of rhizosphere soil microbial community composition from four main plant species to atmospheric N deposition was explored.Nitrogen addition significantly affected rhizosphere soil bacterial community composition of Bothriochloa ischaemum and Stipa bungeana but had no effect on that of Artemisia gmelinii and Cleistogenes squarrosa. Furthermore, it also significantly affected rhizosphere soil fungal community composition of S.bungeana; however, no effects were observed on the other three plant species. Compared with the complexity of rhizosphere soil bacterial network, low N addition rates had a greater effect on the complexity of the rhizosphere soil fungal network.This study revealed that rhizosphere soil bacterial community composition of plant species with a higher niche breadth is generally more sensitive to atmospheric N deposition. Our study highlights the distinct roles of plant species with different niche breadths in maintaining rhizosphere soil microbial community composition under conditions of global N enrichment.Results: Human activities have more than doubled the input of atmospheric reactive nitrogen into terrestrial ecosystems, affecting plant and soil microbial communities. This study aimed to investigate the effect of atmospheric N deposition on the rhizosphere soil microbial community composition of the main plant species with different niches in grassland.An N addition experiment was performed in a semi-arid grassland on the Loess Plateau, China. The experiment consisted of four N addition treatments: N0 (0 g N m−2 yr−1), N3 (3 g N m−2 yr−1), N6 (6 g N m−2 yr−1), and N9 (9 g N m−2 yr−1). The response of rhizosphere soil microbial community composition from four main plant species to atmospheric N deposition was explored.Nitrogen addition significantly affected rhizosphere soil bacterial community composition of Bothriochloa ischaemum and Stipa bungeana but had no effect on that of Artemisia gmelinii and Cleistogenes squarrosa. Furthermore, it also significantly affected rhizosphere soil fungal community composition of S.bungeana; however, no effects were observed on the other three plant species. Compared with the complexity of rhizosphere soil bacterial network, low N addition rates had a greater effect on the complexity of the rhizosphere soil fungal network.This study revealed that rhizosphere soil bacterial community composition of plant species with a higher niche breadth is generally more sensitive to atmospheric N deposition. Our study highlights the distinct roles of plant species with different niche breadths in maintaining rhizosphere soil microbial community composition under conditions of global N enrichment.Conclucsions: Human activities have more than doubled the input of atmospheric reactive nitrogen into terrestrial ecosystems, affecting plant and soil microbial communities. This study aimed to investigate the effect of atmospheric N deposition on the rhizosphere soil microbial community composition of the main plant species with different niches in grassland.An N addition experiment was performed in a semi-arid grassland on the Loess Plateau, China. The experiment consisted of four N addition treatments: N0 (0 g N m−2 yr−1), N3 (3 g N m−2 yr−1), N6 (6 g N m−2 yr−1), and N9 (9 g N m−2 yr−1). The response of rhizosphere soil microbial community composition from four main plant species to atmospheric N deposition was explored.Nitrogen addition significantly affected rhizosphere soil bacterial community composition of Bothriochloa ischaemum and Stipa bungeana but had no effect on that of Artemisia gmelinii and Cleistogenes squarrosa. Furthermore, it also significantly affected rhizosphere soil fungal community composition of S.bungeana; however, no effects were observed on the other three plant species. Compared with the complexity of rhizosphere soil bacterial network, low N addition rates had a greater effect on the complexity of the rhizosphere soil fungal network.This study revealed that rhizosphere soil bacterial community composition of plant species with a higher niche breadth is generally more sensitive to atmospheric N deposition. Our study highlights the distinct roles of plant species with different niche breadths in maintaining rhizosphere soil microbial community composition under conditions of global N enrichment. [ABSTRACT FROM AUTHOR]

Rumor detection is a classification task that utilizes information including the content of a rumor, the comments surrounding it, and information about the users involved. In existing social networks, often emerge unexpectedly and typically concern unknown events, prompting researchers to explore tasks in low-resource situations, including early rumor detection and unknown rumor detection. Existing approaches require constructing rumor representations based on the entire propagation cycle of rumors or specific features of current events. They can lead to models that are too dependent on existing data features and lack generality in low-resource situations and are unsuitable for ever-changing real-world environments. This paper proposes prompt-learning based on few-shot and zero-shot rumor fuzzy detection systems FSRD. These two approaches can detect rumors in a variety of low-resource scenarios (e.g., early rumor detection and unknown rumor detection) using only a small amount of source rumors aided by highly explainable prompt templates, without waiting for rumors to spread to obtain more features. On several standard rumor detection datasets, FSRD outperforms various other detection methods for few-shot scenarios, and achieves competitive results on zero-shot tasks as well. [ABSTRACT FROM AUTHOR]

The deep learning algorithm, which has been increasingly applied in the field of petroleum geophysical prospecting, has achieved good results in improving efficiency and accuracy based on test applications. To play a greater role in actual production, these algorithm modules must be integrated into software systems and used more often in actual production projects. Deep learning frameworks, such as TensorFlow and PyTorch, basically take Python as the core architecture, while the application program mainly uses Java, C#, and other programming languages. During integration, the seismic data read by the Java and C# data interfaces must be transferred to the Python main program module. The data exchange methods between Java, C#, and Python include shared memory, shared directory, and so on. However, these methods have the disadvantages of low transmission efficiency and unsuitability for asynchronous networks. Considering the large volume of seismic data and the need for network support for deep learning, this paper proposes a method of transmitting seismic data based on Socket. By maximizing Socket’s cross-network and efficient longdistance transmission, this approach solves the problem of inefficient transmission of underlying data while integrating the deep learning algorithm module into a software system. Furthermore, the actual production application shows that this method effectively solves the shortage of data transmission in shared memory, shared directory, and other modes while simultaneously improving the transmission efficiency of massive seismic data across modules at the bottom of the software. [ABSTRACT FROM AUTHOR]