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Maize (Zea mays L.) Fusarium ear rot (FER), which is caused by the fungal pathogen Fusarium verticillioides, is a major ear disease affecting maize production in China. Developing resistant maize hybrids is a cost‐effective and environmentally friendly method of reducing yield losses due to FER. Using the nail punch inoculation method, 17 highly resistant and 16 highly susceptible inbred lines were identified in this study, and a large‐scale maize hybrid consisting of 528 F1's was produced using a half‐diallel cross model. The hybrids were inoculated with F. verticillioides in 6 year–location environment combinations during 2020–2022. Our results showed that estimates of genotypic, environmental, and genotype × environment interaction variance components were very significant (p < 0.001). All the crosses generated from highly resistant inbred lines exhibited high kernel resistance, whereas those generated from highly susceptible inbred lines mostly exhibited high susceptibility to FER. Crosses between resistant and susceptible inbred lines showed intermediate resistance to FER. The general combining ability (GCA) effect was greater than the specific combining ability effect across multiple environments, indicating that the inheritance of FER resistance was mainly influenced by additive genetic effects. The correlation coefficient between the expected GCA value and hybrid resistance was 0.789, whereas that between the mid‐parent value and hybrid resistance was only 0.644. Inbred performance per se and their corresponding hybrids across multiple environments were significantly correlated (r = 0.823). However, no relationship was observed between the FER resistance of F1's and that of their parental heterotic groups. Core Ideas: In breeding, selecting parents with Fusarium ear rot (FER) resistance is more crucial than selecting the hybrids themselves.The additive effect is the main genetic component of ear rot in maize, whereas the non‐additive effect is minor.Additional inoculation tests for FER resistance are required for specific hybrid combinations.Hybrid resistance can be inferred based on the general combining ability of parental inbred lines.The additive effects of resistance genes in highly inbred lines are minor. [ABSTRACT FROM AUTHOR]

This paper combines automated scraping of Weibo data and critical discourse analysis to examine the online anti-surrogacy sentiments in China, and finds three nodal points in the interlocking anti-surrogacy discourses. Firstly, through a heterosexual male persona, anti-surrogacy sentiments argue that the legalization of surrogacy would reduce the number of women of marriageable age. Secondly, radical feminist voices argued that surrogacy would make women suffer more in the current gender relations. Finally, gay men were singled out and blamed for exploiting women's body. Through a discussion of these three voices, we argue that despite their apparent diversity, they are fundamentally conditioned and restricted by the state-corporate power axis in China. As a result, they perpetuate current structures of gender and sexuality inequality, instead of engendering meaningful discussions and social change. [ABSTRACT FROM AUTHOR]

This article combines automated scraping of Weibo data and a critical discourse analysis to examine the ways in which online anti-African sentiments produce and amplify the interrelations of racial stigma, sexism and homophobia, as well as misinformation about infectious disease on Chinese social media. The paper finds that three nodal points strongly unite the online anti-African discourse: one, 'unrestrained and promiscuous' African men are carrying the viruses (such as AIDS and/or COVID-19); two, 'unchaste' Chinese women (and occasionally gay men) are receiving the virus; three, there is unidirectional transmission of these viruses from Africans to Chinese. Further, our research findings point to complicated and ambiguous relations between online racist sentiments, state censorship, and China-Africa relations. [ABSTRACT FROM AUTHOR]

Electrocardiographic (ECG) abnormalities are the most intuitive manifestation in the clinical diagnosis of cardiovascular disease. Although significant progress has been achieved in diagnosing ECG abnormalities, current convolutional neural network (CNN) based methods still show a relatively high rate of misdiagnosis. On the one hand, the ECG data an abnormality imbalance problem. On the other hand, the previous work did not consider the multi-label correlations of the ECG abnormalities. The main objective of this paper is to design an ECG abnormal event detection model based on multi-label correlation guided feature fusion. First, the proposed method calculates the correlation of different ECG abnormality labels on the basis of their frequency and Bayesian conditional probability and divides the ECG abnormalities into groups of different levels. The first-level ECG abnormalities are those that have the highest frequency and the second-level abnormalities are those that have a higher probability of other ECG abnormalities given the first-level ECG abnormalities, and so on. Then, the feature fusion strategy uses the multi-label correlation to guide the fusion of features of different levels. Specifically, the features of the first-level ECG signals are integrated into the features of the second-level ECG signals and the features of the former two-level ECG signals are integrated into the features of the third-level ECG signals. Furthermore, a multi-scale receptive field fusion module is proposed to enhance the multi-scale representation ability of the features. To evaluate the performance of the proposed method, it is validated against the 1st China physiological signal challenge dataset, and it achieved better results compared with other state-of-the-art methods. This proposed method achieves a precision score of 0.816, a sensitivity score of 0.845 and an F1-score of 0.827, which are better than other methods. The comparative analysis shows that the proposed method demonstrates the effectiveness in abnormal ECG diagnosis. [ABSTRACT FROM AUTHOR]

Fusarium ear rot (FER) caused by Fusarium verticillioides is one of the most prevalent maize diseases in China and worldwide. Resistance to FER is a complex trait controlled by multiple genes highly affected by environment. In this paper, genome-wide association study (GWAS), bulked sample analysis (BSA), and genomic prediction were performed for understanding FER resistance using 509 diverse inbred lines, which were genotyped by 37,801 high-quality single-nucleotide polymorphisms (SNPs). Ear rot evaluation was performed using artificial inoculation in four environments in China: Xinxiang, Henan, and Shunyi, Beijing, during 2017 and 2018. Significant phenotypic and genetic variation for FER severity was observed, and FER resistance was significantly correlated among the four environments with a generalized heritability of 0.78. GWAS identified 23 SNPs that were associated with FER resistance, 2 of which (1&#95;226233417 on chromosome 1 and 10&#95;14501044 on chromosome 10) were associated at threshold of 2.65 × 10 -7 [-log(0.01/37,801)]. Using BSA, resistance quantitative trait loci were identified on chromosomes 3, 4, 7, 9, and 10 at the 90% confidence level and on chromosomes 3 and 10 at the 95% confidence level. A key region, bin 10.03, was detected by both GWAS and BSA. Genomic prediction for FER resistance showed that the prediction accuracy by trait-related markers was higher than that by randomly selected markers under different levels of marker density. Marker-assisted selection using genomic prediction could be an efficient strategy for genetic improvement for complex traits like FER resistance.