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BackgroundType VI secretion system (T6SS) is widely present in Gram-negative bacteria and directly mediates antagonistic prokaryote interactions. PAAR (proline-alanine-alanine-arginine repeats) proteins have been proven essential for T6SS-mediated secretion and target cell killing. Although PAAR proteins are commonly found in A. baumannii, their biological functions are not fully disclosed yet. In this study, we investigated the functions of a PAAR protein termed TagP (T6SS-associated-gene PAAR), encoded by the gene ACX60_RS09070 outside the core T6SS locus of A. baumannii strain ATCC 17978.MethodsIn this study, tagP null and complement A. baumannii ATCC 17978 strains were constructed. The influence of TagP on T6SS function was investigated through Hcp detection and bacterial competition assay; the influence on environmental fitness was studied through in vitro growth, biofilm formation assay, surface motility assay, survivability in various simulated environmental conditions; the influence on pathogenicity was explored through cell adhesion and invasion assays, intramacrophage survival assay, serum survival assay, and G. melonella Killing assays. Quantitative transcriptomic and proteomic analyses were utilized to observe the global impact of TagP on bacterial status.ResultsCompared with the wildtype strain, the tagP null mutant was impaired in several tested phenotypes such as surface motility, biofilm formation, tolerance to adverse environments, adherence to eukaryotic cells, endurance to serum complement killing, and virulence to Galleria melonella. Notably, although RNA-Seq and proteomics analysis revealed that many genes were significantly down-regulated in the tagP null mutant compared to the wildtype strain, there is no significant difference in their antagonistic abilities. We also found that Histone-like nucleoid structuring protein (H-NS) was significantly upregulated in the tagP null mutant at both mRNA and protein levels.ConclusionsThis study enriches our understanding of the biofunction of PAAR proteins in A. baumannii. The results indicates that TagP involved in a unique modulation of fitness and virulence control in A. baumannii, it is more than a classic PAAR protein involved in T6SS, while how TagP play roles in the fitness and virulence of A. baumannii needs further investigation to clarify.

Yersinia pestis, the causative agent of plague, is a genetically monomorphic bacterial pathogen that evolved from Yersinia pseudotuberculosis approximately 7,400 years ago. We observed unusually frequent mutations in Y. pestis YPO0623, mostly resulting in protein translation termination, which implies a strong natural selection. These mutations were found in all phylogenetic lineages of Y. pestis, and there was no apparent pattern in the spatial distribution of the mutant strains. Based on these findings, we aimed to investigate the biological function of YPO0623 and the reasons for its frequent mutation in Y. pestis. Our in vitro and in vivo assays revealed that the deletion of YPO0623 enhanced the growth of Y. pestis in nutrient-rich environments and led to increased tolerance to heat and cold shocks. With RNA-seq analysis, we also discovered that the deletion of YPO0623 resulted in the upregulation of genes associated with the type VI secretion system (T6SS) at 26°C, which probably plays a crucial role in the response of Y. pestis to environment fluctuations. Furthermore, bioinformatic analysis showed that YPO0623 has high homology with a PLP-dependent aspartate aminotransferase in Salmonella enterica, and the enzyme activity assays confirmed its aspartate aminotransferase activity. However, the enzyme activity of YPO0623 was significantly lower than that in other bacteria. These observations provide some insights into the underlying reasons for the high-frequency nonsense mutations in YPO0623, and further investigations are needed to determine the exact mechanism.

The behavior of pigs is intricately tied to their health status, highlighting the critical importance of accurately recognizing pig behavior, particularly abnormal behavior, for effective health monitoring and management. This study addresses the challenge of accommodating frequent non-rigid deformations in pig behavior using deformable convolutional networks (DCN) to extract more comprehensive features by incorporating offsets during training. To overcome the inherent limitations of traditional DCN offset weight calculations, the study introduces the multi-path coordinate attention (MPCA) mechanism to enhance the optimization of the DCN offset weight calculation within the designed DCN-MPCA module, further integrated into the cross-scale cross-feature (C2f) module of the backbone network. This optimized C2f-DM module significantly enhances feature extraction capabilities. Additionally, a gather-and-distribute (GD) mechanism is employed in the neck to improve non-adjacent layer feature fusion in the YOLOv8 network. Consequently, the novel DM-GD-YOLO model proposed in this study is evaluated on a self-built dataset comprising 11,999 images obtained from an online monitoring platform focusing on pigs aged between 70 and 150 days. The results show that DM-GD-YOLO can simultaneously recognize four common behaviors and three abnormal behaviors, achieving a precision of 88.2%, recall of 92.2%, and mean average precision (mAP) of 95.3% with 6.0MB Parameters and 10.0G FLOPs. Overall, the model outperforms popular models such as Faster R-CNN, EfficientDet, YOLOv7, and YOLOv8 in monitoring pens with about 30 pigs, providing technical support for the intelligent management and welfare-focused breeding of pigs while advancing the transformation and modernization of the pig industry.

Vicia kulingiana, an endemic species, serves as a wild and underutilized vegetable traditionally consumed in China. However, ethnobotanical and chemical studies of this species are not available. This study analyzed its associated ethnobotanical knowledge, nutritional composition and aroma profile. Ethnobotanical surveys revealed its diverse traditional uses, especially as a nutritious vegetable. Further analysis showed V. kulingiana leaves to be high in protein, minerals, vitamin E, and dietary fiber. In total, 165 volatile compounds, such as terpenoids, alcohols, and ketones, were identified. Among them, β-ionone is the most abundant compound with a relative percentage of 8.24%, followed by 2,2,4,6,6-pentamethylheptane (3.2%), 3-(4-methyl-3-pentenyl)furan (2.37%), and linalool (1.68%). Results supported the traditional uses of V. kulingiana’s and highlighted its potential as a valuable food source, encouraging further research on its food applications. The documentation of ethnobotanical knowledge contributes to the conservation of this heritage.

The bacterium Yersinia pestis has developed various strategies to sense and respond to the complex stresses encountered during its transmission and pathogenic processes. PurR is a common transcriptional regulator of purine biosynthesis among microorganisms, and it modulates the transcription level of the pur operon to suppress the production of hypoxanthine nucleotide (IMP). This study aims to understand the functions and regulatory mechanisms of purR in Y. pestis. Firstly, we constructed a purR knockout mutant of Y. pestis strain 201 and compared certain phenotypes of the null mutant (201-ΔpurR) and the wild-type strain (201-WT). The results show that deleting purR has no significant impact on the biofilm formation, growth rate, or viability of Y. pestis under different stress conditions (heat and cold shock, high salinity, and hyperosmotic pressure). Although the cytotoxicity of the purR knockout mutant on HeLa and 293 cells is reduced, the animal-challenging test found no difference of the virulence in mice between 201-ΔpurR and 201-WT. Furthermore, RNA-seq and EMSA analyses demonstrate that PurR binds to the promoter regions of at least 15 genes in Y. pestis strain 201, primarily involved in purine biosynthesis, along with others not previously observed in other bacteria. Additionally, RNA-seq results suggest the presence of 11 potential operons, including a newly identified co-transcriptional T6SS cluster. Thus, aside from its role as a regulator of purine biosynthesis, purR in Y. pestis may have additional regulatory functions.

The existing judicial reading comprehension datasets are relatively simple, and the answers to the questions can be obtained through single-step reasoning. However, the content of legal documents in actual scenarios is complex, making it problematic to infer correct results merely by single-step reasoning. To solve this type of issue, we promote the difficulties of questions included in Chinese Judicial Reading Comprehension (CJRC) dataset and propose two augmented versions, CJRC2.0 and CJRC3.0. These datasets are derived from Chinese judicial judgment documents in different fields and annotated by judicial professionals. Compared to CJRC, there are more types of judgment documents in the two datasets, and the questions become are more challenging to answer. For CJRC2.0, we only preserve complex questions that require to be solved by multi-step reasoning. Besides, we provide additional supporting facts to the answers. For CJRC3.0, we introduce a new question type, the multi-span question, which should be answered by extracting and combining multiple spans in the documents. We implement two powerful baselines to evaluate the difficulty of our proposed datasets. Our proposed datasets fill gaps in the field of explainable legal machine reading comprehension.

Tool wear is a crucial factor in machining as it directly impacts surface quality and indirectly decreases machining efficiency, which leads to significant economic losses. Hence, monitoring tool wear state is of the utmost importance for achieving high performance and efficient machining. Although monitoring tool wear state using a single sensor has been validated in laboratory settings, it has certain drawbacks such as limited feature information acquisition and inability to learn important features adaptively. These limitations pose challenges to quickly extending the monitoring function of tool wear state of the machine tools. To solve these problems, this paper proposes a double-attention deep learning network based on vibroacoustic signal fusion feature infographics. The first solution is the construction of novel infographics using tool-intrinsic characteristics and multi-domain fusion features of multi-sensor inputs, which includes correlation analysis, principal component analysis, and feature fusion. The second solution is to build a novel deep network with a double-attention module and a spatial pyramid pooling module which can accurately and quickly identify tool wear state by successfully extracting critical spatial data from the infographics at various scales. The validity of the network is examined through an interpretability analysis based on the class activation graph. In terms of the tool wear status recognition task, the F1 score of the double-attention model based on an information graph is 11.61% higher than Resnet18, and peak recognition accuracy reaches 97.98%.

Summary: Achieving human-level performance on some of the machine reading comprehension (MRC) datasets is no longer challenging with the help of powerful pre-trained language models (PLMs). However, the internal mechanism of these artifacts remains unclear, placing an obstacle to further understand these models. This paper focuses on conducting a series of analytical experiments to examine the relations between the multi-head self-attention and the final MRC system performance, revealing the potential explainability in PLM-based MRC models. To ensure the robustness of the analyses, we perform our experiments in a multilingual way on top of various PLMs. We discover that passage-to-question and passage understanding attentions are the most important ones in the question answering process, showing strong correlations to the final performance than other parts. Through comprehensive visualizations and case studies, we also observe several general findings on the attention maps, which can be helpful to understand how these models solve the questions.

Achieving human-level performance on some Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs). However, it is necessary to provide both answer prediction and its explanation to further improve the MRC system's reliability, especially for real-life applications. In this paper, we propose a new benchmark called ExpMRC for evaluating the textual explainability of the MRC systems. ExpMRC contains four subsets, including SQuAD, CMRC 2018, RACE+, and C3, with additional annotations of the answer's evidence. The MRC systems are required to give not only the correct answer but also its explanation. We use state-of-the-art PLMs to build baseline systems and adopt various unsupervised approaches to extract both answer and evidence spans without human-annotated evidence spans. The experimental results show that these models are still far from human performance, suggesting that the ExpMRC is challenging. Resources (data and baselines) are available through https://github.com/ymcui/expmrc.

Although prior studies have extensively investigated the effect of working conditions upon professional satisfaction, the cross-national variance in the effect remains largely understudied due to technical or financial restrictions. The Teaching and Learning International Survey (TALIS) provides an opportunity to investigate the cross-country differences in the impact of working conditions upon principals’ professional satisfaction. The current study attempts to investigate the overall and specific effects of principals’ workplace environment satisfaction, rewards satisfaction, and workload stress on their professional satisfaction in 47 countries included in the TALIS 2018. The results indicate that workplace environment, rather than the typically regarded effort-reward issues, is the most powerful contributor to individual principals’ professional satisfaction across 47 countries, while, in countries with low overall professional satisfaction, rewards satisfaction and workload stress are more powerful predictors for principals’ professional satisfaction on the country level. This study may inform policymakers in school principal stimulation and retention that effective practice to stimulate and/or retain school principals may vary across countries and research findings derived from data on the individual level may not be applicable to practice on the country level.

Gonadotropin-releasing hormone (GnRH) is the primary neuropeptide controlling reproduction in vertebrates. GnRH stimulates follicle-stimulating hormone (FSH) and luteinizing hormone (LH) synthesis via a G-protein-coupled receptor, GnRHR, in the pituitary gland. In mammals, GnRHR lacks a C-terminal cytosolic tail (Ctail) and does not exhibit homologous desensitization. This might be an evolutionary adaptation that enables LH surge generation and ovulation. To test this idea, we fused the chicken GnRHR Ctail to the endogenous murine GnRHR in a transgenic model. The LH surge was blunted, but not blocked in these mice. In contrast, they showed reductions in FSH production, ovarian follicle development, and fertility. Addition of the Ctail altered the nature of agonist-induced calcium signaling required for normal FSH production. The loss of the GnRHR Ctail during mammalian evolution is unlikely to have conferred a selective advantage by enabling the LH surge. The adaptive significance of this specialization remains to be determined.