Echinochloa crus-galli var. crus-galli (L.) P. Beauv. (EC), Echinochloa crus-galli var. mitis (Pursh) Petermann (ECM), and Echinochloa glabrescens Munro ex Hook. f. (EG) are all serious rice (Oryza sativa L.) weeds, which are usually treated as a single species in weed management practices. To determine interspecific and intraspecific differences in seed germination responding to different temperatures among the three Echinochloa weeds, we conducted field surveys and collected 66 EC, 141 ECM, and 120 EG populations from rice fields of East China in 2022; and tested their seed germination under 28/15C (day/night), 30/20C, and 35/25C regimes, simulating temperatures of rice planting periods for double-cropping early rice, single-cropping rice, and double-cropping late rice, respectively. In EC, ECM, and EG, seed percentage germination (cumulative percent of germinated seed) and germination index (sum of the ratio of germinated seeds to the corresponding days) increased with increasing temperatures. At 28/15C, the average percentage germination of EC populations (67.5%) was significantly (P < 0.05) higher than ECM (46.4%) and EG (43.7%); GD50 (duration for 50% total germination) for EC populations (5.2 d) was significantly shorter than ECM (5.9 d) and EG (5.8 d). At 35/25C, the percentage germination of EC (90.7%), ECM (80.5%), and EG (80.3%) were all significantly the highest among the three temperature treatments, respectively, and the GD50 values for EC (2.5 d), ECM (2.6 d), and EG (2.7 d) were all significantly the lowest. At 30/20C and 35/25C, average germination percentage of populations collected from transplanted rice fields were significantly higher than that of populations collected from direct-seeded rice fields. Moreover, among EG populations, the longitudes and latitudes of collection locations were significantly correlated with seed percentage germination and germination indices. According to the interspecific differences and intraspecific variations of Echinochloa species, weed management strategies should also be customized according to the species and population characteristics in seed germination.

The emerging era of big data in radio astronomy demands more efficient and higher-quality processing of observational data. While deep learning methods have been applied to tasks such as automatic radio frequency interference (RFI) detection, these methods often face limitations, including dependence on training data and poor generalization, which are also common issues in other deep learning applications within astronomy. In this study, we investigate the use of the open-source image recognition and segmentation model, Segment Anything Model (SAM), and its optimized version, HQ-SAM, due to their impressive generalization capabilities. We evaluate these models across various tasks, including RFI detection and solar radio burst (SRB) identification. For RFI detection, HQ-SAM (SAM) shows performance that is comparable to or even superior to the SumThreshold method, especially with large-area broadband RFI data. In the search for SRBs, HQ-SAM demonstrates strong recognition abilities for Type II and Type III bursts. Overall, with its impressive generalization capability, SAM (HQ-SAM) can be a promising candidate for further optimization and application in RFI and event detection tasks in radio astronomy.

Cross-language internet memes have emerged as a unique and popular mode of online communication, combining bilingual elements with visually textual components. These memes exhibit distinctive characteristics at semantic, syntactic, and pragmatic levels, rendering them a noteworthy semiotic phenomenon in contemporary digital culture. To deepen our understanding of cross-language internet memes, this study investigates user perceptions through a questionnaire, employing SPSS Statistics software for analysis. Applying a social semiotic approach, we decipher the semiotic mechanisms of cross-language memes, shedding light on their potential implications for identity construction. Additionally, we reflect on two prominent trends in internet meme development: the shift from monomodal to multimodal communication and from monolingual to multilingual expressions. This research hopes to provide insights for meme research and online discourse investigations.

The family Streblidae is a significant grouping of dipteran insects within the superfamily Hippoboscoidea, which parasitizes the body surface of bats. With the global spread of bat-related pathogens in recent years, Streblidae has gained increasing attention due to its potential for pathogen transmission. A sample of Brachytarsina amboinensis was sequenced on the B. amboinensis were obtained, compared with available Streblidae mitogenomes, and the phylogeny of Hippoboscoidea was reconstructed. The results indicate that the mitochondrial genome of B. amboinensis exhibits a relatively high degree of conservation, with an identical gene count, arrangement, and orientation as the ancestral insect's genome. Base composition analysis revealed a strong bias towards A and T in the base composition. Selection pressure analysis indicated strong purifying selection acting on cox1. Pairwise genetic distance analysis showed that cox1 evolved at a relatively slow rate. Regarding phylogenetic relationships, the constructed phylogenetic trees using Bayesian inference and Maximum Likelihood methods supported the monophyly of the Hippoboscoidea, Glossinidae, Hippoboscidae, and Nycteribiidae clades, with high nodal support values. Our research confirmed the paraphyly of the families Streblidae. In the familial relations between Nycteribiidae and Streblidae, New World Streblidae share a closer kinship with Nycteribiidae. This contrasts with prior findings which indicated that Old World Streblidae share a closer kinship with Nycteribiidae. This study not only enhances the molecular database for bat flies but also provides a valuable reference for the identification and phylogenetic analysis of Streblidae.

This study aimed to demonstrate the utilization value of 1PN embryos. The 1PN zygotes collected from December 2021 to September 2022 were included in this study. The embryo development, the pronuclear characteristics, and the genetic constitutions were investigated. The overall blastocyst formation and good-quality blastocyst rates in 1PN zygotes were 22.94 and 16.24%, significantly lower than those of 2PN zygotes (63.25 and 50.23%, respectively, P = 0.000). The pronuclear characteristics were found to be correlated with the developmental potential. When comparing 1PN zygotes that developed into blastocysts to those that arrested, the former exhibited a significantly larger area (749.49 ± 142.77 vs. 634.00 ± 119.05, P = 0.000), a longer diameter of pronuclear (29.81 ± 3.08 vs. 27.30 ± 3.00, P = 0.000), and a greater number of nucleolar precursor body (NPB) (11.56 ± 3.84 vs. 7.19 ± 2.73, P = 0.000). Among the tested embryos, the diploidy euploidy rate was significantly higher in blastocysts in comparison with the arrested embryos (66.67 vs. 11.76%, P = 0.000), which was also significantly higher in IVF-1PN blastocysts than in ICSI-1PN blastocysts (75.44 vs. 25.00%, P = 0.001). However, the pronuclear characteristics were not found to be linked to the chromosomal ploidy once they formed blastocysts.

In summary, while the developmental potential of 1PN zygotes is reduced, our study shows that, in addition to the reported pronuclear area and diameter, the number of NPB is also associated with their developmental potential. The 1PN blastocysts exhibit a high diploidy euploidy rate, are recommend to be clinically used post genetic testing, especially for patients who do not have other 2PN embryos available.

We present a practical verification method for safety analysis of the autonomous driving system (ADS). The main idea is to build a surrogate model that quantitatively depicts the behavior of an ADS in the specified traffic scenario. The safety properties proved in the resulting surrogate model apply to the original ADS with a probabilistic guarantee. Given the complexity of a traffic scenario in autonomous driving, our approach further partitions the parameter space of a traffic scenario for the ADS into safe sub-spaces with varying levels of guarantees and unsafe sub-spaces with confirmed counter-examples. Innovatively, the partitioning is based on a branching algorithm that features explainable AI methods. We demonstrate the utility of the proposed approach by evaluating safety properties on the state-of-the-art ADS Interfuser, with a variety of simulated traffic scenarios, and we show that our approach and existing ADS testing work complement each other. We certify 5 safe scenarios from the verification results and find out 3 sneaky behavior discrepancies in Interfuser which can hardly be detected by safety testing approaches.