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Focusing on the low level of management and informatization and intelligence of the beef cattle industry in China, a big data platform for commercial beef cattle breeding, referring to the experience of international advanced beef cattle breeding countries, was proposed in this research. The functions of the platform includes integrating germplasm resources of beef cattle, automatic collecting of key beef cattle breeding traits, full-service support for the beef cattle breeding process, formation of big data analysis and decision-making system for beef cattle germplasm resources, and joint breeding innovation model. Aiming at the backward storage and sharing methods of beef cattle breeding data and incomplete information records in China, an information resource integration platform and an information database for beef cattle germplasm were established. Due to the vagueness and subjectivity of the breeding performance evaluation standard, a scientific online evaluation technology of beef cattle breeding traits and a non-contact automatic acquisition and intelligent calculation method were proposed. Considering the lack of scientific and systematic breeding planning and guidance for farmers in China, a full-service support system for beef cattle breeding and nanny-style breeding guidance during beef cattle breeding was developed. And an interactive progressive decision-making method for beef cattle breeding to solve the lack of data accumulation of beef cattle germplasm was proposed. The main body of breeding and farming enterprises was not closely integrated, according to that, the innovative breeding model of regional integration was explored. The idea of commercial beef cattle breeding big data software platform and the technological and model innovation content were also introduced. The technology innovations included the deep mining of germplasm resources data and improved breed management pedigree, the automatic acquisition and evaluation technology of non-contact breeding traits, the fusion of multi-source heterogeneous information to provide intelligent decision support. The future goal is to form a sustainable information solution for China's beef cattle breeding industry and improve the overall level of China's beef cattle breeding industry.

Simple Summary: A-band ultraviolet (UVA) in solar radiation plays an important role in the natural decay of airborne bacteria. In this study, laboratory simulation experiments were conducted to investigate the impact of varied UVA intensities on the decay of airborne bacteria and to analyze the potential use of UVA to reduce indoor airborne bacteria in cattle houses. Decay and inactive rates of airborne bacteria from cattle sources increased with the UVA radiation intensities when the radiation was over 1500 μW cm−2, and it was affected by the initial concentration and radiation durations. Results of this study provide the foundation for further applying UVA as an alternative way to reduce indoor airborne bacteria in animal houses. Inspired by the effects of solar or UV radiation on the decay of airborne bacteria during their transport, this study investigated the effect of UVA on the decay of airborne bacteria from cattle houses and analyzed the potential use of UVA to reduce indoor airborne bacteria under laboratory conditions. Airborne bacteria from the cattle source were generated and released into a small-scale test chamber (1.5 m3) with different strategies according to the different objectives in decay tests and simulated sterilization tests. Increasing with the UVA radiation gradients (0, 500, 1000, 1500 μW cm−2), the average decay rate of total curable airborne bacteria ranged from 2.7% to 61.6% in decay tests. Under the combination of different UVA radiation intensities (2000 μW cm−2 in maximum) and radiation durations (60 min in maximum), simulated sterilization tests were conducted to examine the potential use of UVA radiation for air sterilization in animal houses. With the dynamic inactive rate (DIR) ranging from 17.2% to 62.4%, we proved that UVA may be an alternative way to reduce the indoor airborne bacteria in cattle houses if applied properly. Similar effects would be achieved using either a high radiation intensity with a short radiation duration or a low radiation intensity with a long radiation duration. [ABSTRACT FROM AUTHOR]

Featured Application: This study proposes a multiple fine-grained nested named entity recognition model, which provides a solution for other specialized fields and lays the foundation for subsequent knowledge graph construction and intelligent inquiry system construction. Extracting entities from large volumes of chicken epidemic texts is crucial for knowledge sharing, integration, and application. However, named entity recognition (NER) encounters significant challenges in this domain, particularly due to the prevalence of nested entities and domain-specific named entities, coupled with a scarcity of labeled data. To address these challenges, we compiled a corpus from 50 books on chicken diseases, covering 28 different disease types. Utilizing this corpus, we constructed the CDNER dataset and developed a nested NER model, MFGFF-BiLSTM-EGP. This model integrates the multiple fine-grained feature fusion (MFGFF) module with a BiLSTM neural network and employs an efficient global pointer (EGP) to predict the entity location encoding. In the MFGFF module, we designed three encoders: the character encoder, word encoder, and sentence encoder. This design effectively captured fine-grained features and improved the recognition accuracy of nested entities. Experimental results showed that the model performed robustly, with F1 scores of 91.98%, 73.32%, and 82.54% on the CDNER, CMeEE V2, and CLUENER datasets, respectively, outperforming other commonly used NER models. Specifically, on the CDNER dataset, the model achieved an F1 score of 79.68% for nested entity recognition. This research not only advances the development of a knowledge graph and intelligent question-answering system for chicken diseases, but also provides a viable solution for extracting disease information that can be applied to other livestock species. [ABSTRACT FROM AUTHOR]

Sirtuin 5 (SIRT5), localized in the mitochondria, has been identified as a protein desuccinylase and demalonylase in the mitochondria since the depletion of SIRT5 boosted the global succinylation and malonylation of mitochondrial proteins. We investigated the role of SIRT5 in diabetic cardiomyopathy (DCM) and identified the mechanism regarding lysine demalonylation in this process. Wild-type and SIRT5 knockout mice were induced with DCM, and primary cardiomyocytes and cardiac fibroblasts extracted from wild-type and SIRT5 knockout mice were subjected to high glucose (HG). SIRT5 deficiency exacerbated myocardial injury in DCM mice, aggravated HG-induced oxidative stress and mitochondrial dysfunction in cardiomyocytes, and intensified cardiomyocyte senescence, pyroptosis, and DNA damage. DCM-induced SIRT5 loss diminished glutathione S-transferase P (GSTP1) protein stability, represented by significantly increased lysine malonylation (Mal-Lys) modification of GSTP1. SIRT5 overexpression alleviated DCM-related myocardial injury, which was reversed by GSTP1 knockdown. Reduced SIRT5 transcription in DCM resulted from the downregulation of SPI1. SPI1 promoted the transcription of SIRT5, thereby ameliorating DCM-associated myocardial injury. However, SIRT5 deletion resulted in a significant reversal of the protective effect of SPI1. These observations suggest that SPI1 activates SIRT5 transcriptionally to mediate GSTP1 Mal-Lys modification and protein stability, thus ameliorating DCM-associated myocardial injury., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)