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Context: Muscle fibre characteristics are important internal factors that can directly affect pork quality. Especially muscle-fibre types can interconvert with the influence of certain factors. Aims: The purpose of this experiment was to study the differences in meat quality among Songliao black pigs, Jilin Hua pigs and Dongliao black pigs, and the molecular mechanism of the differences. Methods: First, the conventional meat-quality traits of each breed were determined. Second, the muscle-fibre characteristics of different muscles were analysed by the haematoxylin–eosin staining and immunohistochemistry. Next, the activity of several key oxidative/glycolytic enzymes was detected with kits. Finally, quantitative polymerase chain reaction was used to analyse the expression abundance of myosin heavy-chain (MyHC) genes and key genes related to muscle fibre-type transformation. Key results: The results of meat-quality measurement showed that Songliao black pigs were superior to Jilin Hua pigs and Dongliao black pigs in water-holding capacity, tenderness and intra-muscular fat; and Jilin Hua pigs were superior to Dongliao black pigs in water-holding capacity. The diameter and area of muscle fibres were the smallest, the density was the largest, the proportion of oxidative muscle fibres, oxidative enzyme activity and expression abundance of oxidative genes were the highest in Songliao black pigs, followed by Jilin Hua pigs and Dongliao black pigs. The proportions of oxidative muscle fibres ranged from 10.37% to 33.6% in Songliao black pigs, from 6.96% to 26.42% in Jilin Hua pigs, and from 5.86% to 17.42% in Dongliao black pigs. The psoas major exhibited the smallest muscle-fibre diameter, the highest density, followed by triceps brachii , biceps femoris and longissimus thoracis. The oxidative muscle fibre proportions of the psoas major and triceps brachii were significantly greater than those of the biceps femoris and longissimus thoracis. The expression abundances of AMP-activated protein kinase (AMPK), silencing information regulator 1 (SIRT1) and peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α) (Sirt1/AMPK/PGC-1α) pathway and mitochondrial function-related genes were the highest in Songliao black pigs, followed by Jilin Hua pigs and Dongliao black pigs. Conclusions: The results showed that Songliao black pigs had the best meat quality, followed by Jilin Hua pigs and Dongliao black pigs. The meat quality of psoas major and triceps brachii was significantly greater than that of biceps femoris and longissimus thoracis. This experiment suggests that transformation of oxidative muscle fibres can be promoted through the Sirt1/AMPK/PGC-1α pathway. Implications: By analysing the meat-quality traits and the molecular mechanism of meat-quality differences, it can provide data reference and direction for further meat-quality improvement of three breed pigs. We systematically studied the differences in meat quality among Songliao black pigs, Jilin Hua pigs and Dongliao black pigs, from the perspective of meat quality, muscle-fibre characteristics and Sirt1/AMPK/PGC-1α signalling pathways. In addition, muscle-fibre characteristics of different parts (longissimus dorsi , psoas major , triceps brachii and biceps femur) of each breed were comprehensively studied instead of a single tissue. The results of oxidised muscle fibres were verified by immunohistochemistry, enzyme-activity assay and gene-expression methods. [ABSTRACT FROM AUTHOR]

We investigate the effect of corporate digitalization capabilities on green innovation among Chinese‐listed firms. Using a panel dataset of 2908 companies from 2011 to 2020, we use textual analysis and entropy weighting on corporate annual reports to construct a yearly corporate digitalization index. Our findings show that corporate digitalization promotes green innovation, as evidenced by patent applications and grants. This relationship is stronger for firms with fewer financial constraints and in provinces with strong intellectual property protection. We also find the national digital policy of the "Internet Plus" strategy has a stronger positive effect on corporate green innovation for corporations with a higher degree of digitalization. Our results are robust to various alternative measures, econometric models, and different samples. [ABSTRACT FROM AUTHOR]

Highly interactive honeypots can form reliable connections by responding to attackers to delay and capture intranet attacks. However, current research focuses on modeling the attacker as part of the environment and defining single-step attack actions by simulation to study the interaction of honeypots. It ignores the iterative nature of the attack and defense game, which is inconsistent with the correlative and sequential nature of actions in real attacks. These limitations lead to insufficient interaction of the honeypot response strategies generated by the study, making it difficult to support effective and continuous games with attack behaviors. In this paper, we propose an autonomous attack response framework (named AARF) to enhance interaction based on multi-agent dynamic games. AARF consists of three parts: a virtual honeynet environment, attack agents, and defense agents. Attack agents are modeled to generate multi-step attack chains based on a Hidden Markov Model (HMM) combined with the generic threat framework ATT&CK (Adversarial Tactics, Techniques, and Common Knowledge). The defense agents iteratively interact with the attack behavior chain based on reinforcement learning (RL) to learn to generate honeypot optimal response strategies. Aiming at the sample utilization inefficiency problem of random uniform sampling widely used in RL, we propose the dynamic value label sampling (DVLS) method in the dynamic environment. DVLS can effectively improve the sample utilization during the experience replay phase and thus improve the learning efficiency of honeypot agents under the RL framework. We further couple it with a classic DQN to replace the traditional random uniform sampling method. Based on AARF, we instantiate different functional honeypot models for deception in intranet scenarios. In the simulation environment, honeypots collaboratively respond to multi-step intranet attack chains to defend against these attacks, which demonstrates the effectiveness of AARF. The average cumulative reward of the DQN with DVLS is beyond eight percent, and the convergence speed is improved by five percent compared to a classic DQN. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) represents a substantial global health challenge, with its impact on mortality and morbidity rates surpassing that of cancer. The present study was designed to explore the cardioprotective properties of anthocyanin (ACN), a compound derived from black barley, against oxidative stress-induced damage in myocardial cells and to uncover the molecular mechanisms at play. Utilizing both in vitro and in vivo experimental models, our findings indicate that ACN notably reduced cell damage caused by oxidative stress and effectively prevented apoptosis. High-throughput RNA sequencing analysis has shed light on the mechanism by which ACN achieves its antioxidative stress effects, implicating the PTEN-Akt signaling pathway. ACN was found to modulate PTEN expression levels, which in turn influences the Akt pathway, leading to a reduction in apoptotic processes. This novel insight lays the groundwork for the potential clinical utilization of ACN in the management of CVD. While this study has shed light on some of the functions of ACN, it is important to recognize that natural compounds often interact with multiple molecular targets and engage in intricate signaling cascades. Future research endeavors will concentrate on further elucidating the regulatory mechanisms by which ACN influences PTEN expression, with the goal of enhancing our comprehension and expanding the therapeutic potential of ACN in the treatment of cardiovascular conditions. [ABSTRACT FROM AUTHOR]

Sandy loamy soils are widely distributed in fluvial floodplains and experience flooding events frequently, resulting in a large amount of nitrous oxide (N2O) emissions. This case is more serious when the soil use is changed to paddies. It is of great significance to figure out the N2O consumption and its influencing factors in sandy loamy paddy soils to mitigate N2O emissions. In this study, three sandy loamy paddy soils (0–5 cm) originated from lake deposits were selected (S1, S2, and S3) as objectives. A certain concentration of exogenous N2O was added at the bottom of the flooded soil column to monitor the dynamics of N2O and nitrogen (N2) on the soil surface. Total N2O consumption, N2O uptake, and N2 production were quantified, and the abundance of nitrous oxide reductase genes (nosZI, and nosZII) and other soil properties (ammonium‐nitrogen, nitrate‐nitrogen, and dissolved organic carbon [DOC] content) were analyzed. The results showed that the sandy loamy paddy soil column with a depth of 0–5 cm could intercept more than 95% of the exogenous N2O under the flooded anaerobic condition, indicating that the three sandy loamy paddy soils all had extremely strong N2O consumption capacities. And the increment of N2 accounted for 68.73%–76.09% of the total N2O consumption, which had a stronger relationship with the increase of nosZI gene abundance than nosZII gene. In addition, the total N2O consumption and N2 increment of S1 and S3 soils were significantly higher than those of S2 soil. This difference was mainly related to soil organic matter content, total nitrogen content, DOC consumption, and the increase of nosZI gene abundance (p < 0.05). The strong N2O consumption potential of sandy loamy soils can provide feasible solutions for regulating N2O emissions in a wide range of similar environments in fluvial floodplains. Core Ideas: Over 95% of N2O in 5‐cm‐soil depth was consumed by flooded sandy loamy paddy soils.The microbial reduction of N2O to N2 is the main pathway of N2O consumption.N2O consumption and N2 increment dominated by nosZI‐containing microorganisms. [ABSTRACT FROM AUTHOR]

Vulnerabilities are often accompanied by cyberattacks. CVE is the largest repository of open vulnerabilities, which keeps expanding. ATT&CK models known multi-step attacks both tactically and technically and remains up to date. It is valuable to correlate the vulnerability in CVE with the corresponding tactic and technique of ATT&CK which exploit the vulnerability, for active defense. Mappings manually is not only time-consuming but also difficult to keep up-to-date. Existing language-based automated mapping methods do not utilize the information associated with attack behaviors outside of CVE and ATT&CK and are therefore ineffective. In this paper, we propose a novel framework named VTT-LLM for mapping Vulnerabilities to Tactics and Techniques based on Large Language Models, which consists of a generation model and a mapping model. In order to generate fine-tuning instructions for LLM, we create a template to extract knowledge of CWE (a standardized list of common weaknesses) and CAPEC (a standardized list of common attack patterns). We train the generation model of VTT-LLM by fine-tuning the LLM according to the above instructions. The generation model correlates vulnerability and attack through their descriptions. The mapping model transforms the descriptions of ATT&CK tactics and techniques into vectors through text embedding and further associates them with attacks through semantic matching. By leveraging the knowledge of CWE and CAPEC, VTT-LLM can eventually automate the process of linking vulnerabilities in CVE to the attack techniques and tactics of ATT&CK. Experiments on the latest public dataset, ChatGPT-VDMEval, show the effectiveness of VTT-LLM with an accuracy of 85.18%, which is 13.69% and 54.42% higher than the existing CVET and ChatGPT-based methods, respectively. In addition, compared to fine-tuning without outside knowledge, the accuracy of VTT-LLM with chain fine-tuning is 9.24% higher on average across different LLMs. [ABSTRACT FROM AUTHOR]