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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]

To investigate whether whole-body vibration (WBV) plus hip-knee muscle strengthening is more efficient in relieving pain and improving function than hip-knee strengthening alone. Thirty-six participants with patellofemoral pain syndrome (PFPS) were recruited and randomly allocated to either the (1) hip-knee strengthening only (HK group, n = 18) or (2) WBV plus hip-knee strengthening group (WHK group, n = 18). All participants attended 18 physiotherapy sessions (3 sessions/week, 40 min/session) over 6 weeks. Data on symptoms, function, surface electromyography (sEMG) signals from the vastus medialis and gluteus medius, and quality of life were evaluated at baseline (T0), 6 weeks after (T6), and the 12-week follow-up (T18). Significant group × time interactions were found for the VAS score (p < 0.001) and vastus medialis performance (p ≤ 0.015). The WHK group exhibited a greater pain relief than did the HK group at T18 (p ≤ 0.014). The WHK group exhibited significantly larger improvements in the RMS value than did the HK group at T6 (p ≤ 0.011). The present study shows that 6 weeks of WBV plus hip-knee strengthening can improve vastus medialis performance and maintain long-term pain relief to a significantly greater extent than can hip-knee strengthening alone. The present study shows that 6 weeks of WBV plus hip-knee strengthening can improve vastus medialis performance and maintain long-term pain relief to a significantly greater extent than can hip-knee strengthening alone. [ABSTRACT FROM AUTHOR]

Vocalizations of farm animals may accompany particular states of animals' mood or emotion. Based on these vocalizations, we can judge animals' current needs and impaired welfare, so they may be regarded as indicators of animals' state of welfare. However, the noise made by different mechanical systems in the commercial poultry house can interfere with the detection of laying hens' vocalization. The purpose of this study is to analyze and classify vocalizations of laying hens and mechanical noises. The analysis and classification is based on time-domain and frequency-domain characteristics of the signal. Vocalization in the egg laying process and song are two kinds of typical laying hens' vocalizations. Mechanical sources of noise on the farm mainly include the ventilation system, manure-removal systems, egg-collection systems, and feeding systems. The power spectral density and sub-band power ratio of laying hens' vocalizations and mechanical noises were extracted by using a sound analysis system based on the program development environment LabVIEW. A J48 decision tree algorithm was used to classify and identify laying hens' vocalization and mechanical noise on the data-mining platform Weka. The results showed that the frequency ranges of vocalization associated with the egg-laying process and singing were mainly distributed within 400-2 500 Hz, the frequency ranges of ventilation-system noise and feeding system noise were mainly distributed below 1 500 Hz, the frequency ranges of manure-removal system noise and egg-collection system noise were located within 100-3 000 Hz, which was wider than the frequency ranges of other sounds. The max power ratios of vocalization in the egg-laying process and singing were (83.4±9.9)% and (76.7±18.8)%, which were within the frequency range >689-1 378 Hz;. The power ratios of vocalization in the egg laying process and singing were higher than that of mechanical noises in the frequency range >689-1 378 Hz. The maximum power ratios of ventilation-system and feeding-system noise were 68.1±2.1% and 74.5±9.7%, respectively, which were within the frequency range 0-689 Hz. The power ratios of ventilation-system and feeding-system noise were higher than that of others in the frequency range 0-689 Hz. The power ratio of manure-removal system and egg-collection system noise were relatively uniform; the maximum power ratios were just 37.2±4.1% and 40.9±3.4%, respectively, and were within the frequency range >1 378-2 756 Hz. The power ratios of manure-removal system and egg-collection system noise were higher than that of others in the frequency range >1 378-2 756 Hz. The sound recognition model based on sub-band power ratio had an average correct classification rate of 93.4%. Further, 92.5% of vocalizations associated with the egg laying process and 85.9% of songs were correctly identified, and the correct classification ratios of ventilation system, feeding system, manure-removal system, and egg-collection system noise were 97.7%, 96.2%, 97.8%, and 94.4%, which were higher than that of laying hens' vocalizations. This method, based on sub-band power ratios, effectively recognizes and discriminates noise from different sources, which can provide a reference for detecting vocalizations of animals within the complex noise environment on the commercial farm. [ABSTRACT FROM AUTHOR]

Vocalization, which plays an important role in the communication of many animals, could be regarded as an easy way to evaluate the current needs of animals and their state of impaired welfare by non-invasive and continuous monitoring. Modern techniques of sound analysis have provided tools for the quantitative description and statistical analysis of animal vocalization. However, a farmer always regulates the temperature and exchanges fresh air through mechanical ventilation in a poultry house, so that there is often low-frequency fan noise which can complicate the time-frequency features of poultry sound. In order to extract useful sound signals from the laying hens' sounds including fan noise, a digitized sound acquisition platform was built to record the pure sounds of laying hens (Hy-Line Variety Brown), the simple fan noise, and the mixed sounds of them both separately. All data processing and analysis were completed in LabVIEW. First, the time-frequency features of the pure sounds of laying hens, the simple fan noise, and the mixed sounds were analyzed in turn. Then the analog signals of different signal to noise ratios (SNR) were constructed by using the clean sounds of laying hens and fan noise. Finally, the effects of removing fan noise from the analog sound and field sound of laying hens were compared by 2 filters (Infinite Impulse Response, IIR, and Finite Impulse Response, FIR) and 3 wavelet de-noising methods (Stein's Unbiased Risk Estimation, SURE, Universal and Minimax). The results showed that although the characteristic parameters of laying hens' vocalization at different growth stages and in different rearing environments were disparate, the main frequency range of the sound of laying hens in the laying period was about 400-2 500 Hz, while the frequency range of fan noise was under 600 Hz. Root mean square errors (RMSE) of the filters under different SNRs were larger than that of the wavelet de-noising methods. The RMSE of the IIR filter was less than that of the FIR filter, which means that the band pass filter of IIR had a better filtering effect than that of FIR. The RMSE of the wavelet de-noising method based on SURE was at a minimum in three wavelet de-noising methods, which means that the wavelet de-noising method using SURE for threshold selection yielded better de-noising performance than that of using the others for threshold selection. [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).)

The COVID-19 pandemic, caused by the coronavirus, SARS-CoV-2, has claimed millions of lives worldwide in the past two years. Fatalities among the elderly with underlying cardiovascular disease, lung disease, and diabetes have particularly been high. A bibliometrics analysis on author's keywords was carried out, and searched for possible links between various coronavirus studies over the past 50 years, and integrated them. We found keywords like immune system, immunity, nutrition, malnutrition, micronutrients, exercise, inflammation, and hyperinflammation were highly related to each other. Based on these findings, we hypothesized that the human immune system is a multilevel super complex system, which employs multiple strategies to contain microorganism infections and restore homeostasis. It was also found that the behavior of the immune system is not able to be described by a single immunological theory. However, one main strategy is "self-destroy and rebuild", which consists of a series of inflammatory responses: 1) active self-destruction of damaged/dysfunctional somatic cells; 2) removal of debris and cells; 3) rebuilding tissues. Thus, invading microorganisms' clearance could be only a passive bystander response to this destroy-rebuild process. Microbial infections could be self-limiting and promoted as an indispensable essential nutrition for the vast number of genes existing in the microorganisms. The transient nutrition surge resulting from the degradation of the self-destroyed cell debris coupled with the existing nutrition state in the patient may play an important role in the pathogenesis of COVID-19. Finally, a few possible coping strategies to mitigate COVID-19, including vaccination, are discussed., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)