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The main purpose of this Special Issue is to provide readers with current understandings of the interactions and causal relations among injury stimuli (including microorganism infections), immune response and overnutrition/lipotoxicity in disease pathogenesis. Special emphasis is placed on microbiota infection, cell self-destruction in response to inflammation, metabolic homeostasis and transient overnutrition in disease initiation, progression and resolution through the dissipation of nutrients for cell metabolism and tissue regeneration. The process of breaking down damaged cells and converting them into various macronutrients for tissue regeneration and cell metabolism is one of the most important functions of the immune system in maintaining health. Owing to the self-destructive inflammatory response and the swift and efficient removal of dead cell debris by efferocytosis [[15]], most microorganism infections are self-limiting, and inflammation is often asymptomatic, even though these microorganisms also cause harm to human somatic cells. [Extracted from the article]

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]

COVID-19 continues to be an ongoing global threat. The elderly with underlying health conditions like cardiovascular and lung diseases, diabetes, obesity, are the most vulnerable to this disease. Curing the pre-existing health conditions will greatly increase a person's resilience to COVID-19 and lower the death rate of the old people. Digestion and immunity form an integrated nutrition acquisition process, especially in obtaining essential amino acids and essential fatty acids from living microbial cells. A mature strong immunity coupled with gut dysbiosis in adults is the main cause of nutritional disorders like morbid obesity, diabetes mellitus, cardiovascular and pulmonary diseases. Nutrition disorders in return worsen dysbiosis. Human microbiome has an intrinsic duality. While a diverse microbiome provides a full spectrum of essential nutrients to our body, nutrition disorders fuel overgrowth of microbiota (dysbiosis) at many sites on or inside our body, and are the main causes of chronic inflammation at these sites. In the case of COVID-19, nutritional disorder impairs the immunity, causes hyperinflammation, and leads to the protracted overload of cytokines by the immune system, i.e., the cytokine storm. Autophagy induced by restrictive eating is an ideal inhibitor of microbiota overgrowth, as autophagy deprives microbiota of excessive nutrition for replication. Autophagy also attenuates inflammation. Therefore, as a precaution, the author suggests restoring good health in the elderly with the support from a diverse gut microbiome and daily regular food intake restriction, so as to lower the risk of developing into severe case even if they are infected by COVID-19. [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]

Simple Summary: The H9N2 avian influenza virus has spread to the whole world and become one of the dominant subtype influenza viruses in chickens in China. H9N2 virus could not only result in great economic losses by reducing egg production but also serve as a gene vector to provide its gene segments to other emerging severe influenza A viruses to cause higher mortality and more serious consequences. Thus, developing models to predict H9N2 status should be given priority. Our main aim was to use the machine learning method (XGBoost classification algorithm) and regular production data (laying rate and mortality) to predict the occurrence and development of H9N2 in laying hen houses and evaluate their performance in disease prediction. Additionally, we assessed the working ability of the framework with different time frames to predict H9N2 status in advance within a 3-day period. We found that this framework could work well in prediction with high accuracy and sensitivity, and with more information introduced into the model, more "don't care values" would be added into datasets to affect model performance by forming attribute noise. Besides, our study recommended efficient frameworks and models for H9N2 status prediction and provided practical potential uses in the livestock and poultry industry. The H9N2 avian influenza virus has become one of the dominant subtypes of avian influenza virus in poultry and has been significantly harmful to chickens in China, with great economic losses in terms of reduced egg production or high mortality by co-infection with other pathogens. A prediction of H9N2 status based on easily available production data with high accuracy would be important and essential to prevent and control H9N2 outbreaks in advance. This study developed a machine learning framework based on the XGBoost classification algorithm using 3 months' laying rates and mortalities collected from three H9N2-infected laying hen houses with complete onset cycles. A framework was developed to automatically predict the H9N2 status of individual house for future 3 days (H9N2 status + 0, H9N2 status + 1, H9N2 status + 2) with five time frames (day + 0, day − 1, day − 2, day − 3, day − 4). It had been proven that a high accuracy rate > 90%, a recall rate > 90%, a precision rate of >80%, and an area under the curve of the receiver operator characteristic ≥ 0.85 could be achieved with the prediction models. Models with day + 0 and day − 1 were highly recommended to predict H9N2 status + 0 and H9N2 status + 1 for the direct or auxiliary monitoring of its occurrence and development. Such a framework could provide new insights into predicting H9N2 outbreaks, and other practical potential applications to assist in disease monitor were also considerable. [ABSTRACT FROM AUTHOR]

Abstract: The major hurdle that limits extensive application of thermal sprayed WC–Co coatings is the undesirable phase changes from WC to W2C or even W due to loss of carbon. This study investigated quantitatively the decarburization of WC in both in-flight and coating formation stages during atmospheric plasma spraying. The microstructure evolution of WC particles accompanying the decarburization was also investigated. Results showed that decomposition and oxidation of WC to W2C predominantly occurred in the in-flight stage (75.82%), while remnant 24.18% occurred during the coating formation stage. Further carbon loss during the coating formation stage of WC–Co droplets is dependent significantly on the temperature of the substrate/pre-coating. Most of the irregular WC particles turned into rounded morphology with a significant decrease in size. W2C precipitated from the liquid matrix showed either columnar growth or epitaxial growth on WC substrate. [Copyright &y& Elsevier]

To improve the classification of pig vocalization using vocal signals and improve recognition accuracy, a pig vocalization classification method based on multi-feature fusion is proposed in this study. With the typical vocalization of pigs in large-scale breeding houses as the research object, short-time energy, frequency centroid, formant frequency and first-order difference, and Mel frequency cepstral coefficient and first-order difference were extracted as the fusion features. These fusion features were improved using principal component analysis. A pig vocalization classification model with a BP neural network optimized based on the genetic algorithm was constructed. The results showed that using the improved features to recognize pig grunting, squealing, and coughing, the average recognition accuracy was 93.2%; the recognition precisions were 87.9%, 98.1%, and 92.7%, respectively, with an average of 92.9%; and the recognition recalls were 92.0%, 99.1%, and 87.4%, respectively, with an average of 92.8%, which indicated that the proposed pig vocalization classification method had good recognition precision and recall, and could provide a reference for pig vocalization information feedback and automatic recognition. [ABSTRACT FROM AUTHOR]

Background: Dysbiosis of gut microbiota is frequent in liver cirrhosis (LC) patients, and splenectomy (SP) has been reported to improve LC. Herein, we report the effects of SP on gut microbiota, especially on Veillonella parvula, a Gram-negative coccus of the gastrointestinal tract, in LC mice, and the underlying mechanism. Methods: LC mice models were induced by tail vein injection of concanavalin A (ConA), followed by SP. 16 s rRNA sequencing was conducted to analyze the effects of ConA induction and SP on mouse gut microbiota and the gene expression affected by gut microbiota. LC mice receiving SP were gavaged with Veillonella parvula. Likewise, hepatic stellate cells (HSC) and hepatocytes (HC) were induced with conditioned medium (CM) of Veillonella parvula. Results: SP alleviated LC in mice by restoring gut barrier function and maintaining gut microbiota balance, with Veillonella as the key genus. The Veillonella parvula gavage on LC mice reversed the ameliorative effect of SP. The CM of Veillonella parvula promoted the activation of HSC and the release of IL-6, IL-1β, and TNF-α. Also, the CM of Veillonella parvula induced HC pyroptosis and the release of ALT and AST. Veillonella parvula represented an imbalance in the gut microbiota, thus enhancing gut-derived endotoxins in the liver with the main target being Tlr4/Nlrp3. Inhibition of Tlr4 blocked Veillonella parvula-induced HC damage, HSC activation, and subsequent LC progression. Conclusion: SP-mediated gut microbiota regulation ameliorates ConA-related LC progression by inhibiting Tlr4/Nlrp3 in the liver. [ABSTRACT FROM AUTHOR]

To improve the detection speed of cow mounting behavior and the lightness of the model in dense scenes, this study proposes a lightweight rapid detection system for cow mounting behavior. Using the concept of EfficientNetV2, a lightweight backbone network is designed using an attention mechanism, inverted residual structure, and depth-wise separable convolution. Next, a feature enhancement module is designed using residual structure, efficient attention mechanism, and Ghost convolution. Finally, YOLOv5s, the lightweight backbone network, and the feature enhancement module are combined to construct a lightweight rapid recognition model for cow mounting behavior. Multiple cameras were installed in a barn with 200 cows to obtain 3343 images that formed the cow mounting behavior dataset. Based on the experimental results, the inference speed of the model put forward in this study is as high as 333.3 fps, the inference time per image is 4.1 ms, and the model mAP value is 87.7%. The mAP value of the proposed model is shown to be 2.1% higher than that of YOLOv5s, the inference speed is 0.47 times greater than that of YOLOv5s, and the model weight is 2.34 times less than that of YOLOv5s. According to the obtained results, the model proposed in the current work shows high accuracy and inference speed and acquires the automatic detection of cow mounting behavior in dense scenes, which would be beneficial for the all-weather real-time monitoring of multi-channel cameras in large cattle farms. [ABSTRACT FROM AUTHOR]

Human beings have co-evolved with the microorganisms in our environment for millions of years, and have developed into a symbiosis in a mutually beneficial/defensive way. Human beings have significant multifaceted relationships with the diverse microbial community. Apart from the important protective role of microbial community exposure in development of early immunity, millions of inimitable bacterial genes of the diverse microbial community are the indispensable source of essential nutrients like essential amino acids and essential fatty acids for human body. The essential nutrition from microbiome is harvested through xenophagy. As an immune effector, xenophagy will capture any microorganisms that touch the epithelial cells of our gastrointestinal tract, degrade them and turn them into nutrients for the use of our body. [ABSTRACT FROM AUTHOR]

To improve the detection speed of cow mounting behavior and the lightness of the model in dense scenes, this study proposes a lightweight rapid detection system for cow mounting behavior. Using the concept of EfficientNetV2, a lightweight backbone network is designed using an attention mechanism, inverted residual structure, and depth-wise separable convolution. Next, a feature enhancement module is designed using residual structure, efficient attention mechanism, and Ghost convolution. Finally, YOLOv5s, the lightweight backbone network, and the feature enhancement module are combined to construct a lightweight rapid recognition model for cow mounting behavior. Multiple cameras were installed in a barn with 200 cows to obtain 3343 images that formed the cow mounting behavior dataset. Based on the experimental results, the inference speed of the model put forward in this study is as high as 333.3 fps, the inference time per image is 4.1 ms, and the model mAP value is 87.7%. The mAP value of the proposed model is shown to be 2.1% higher than that of YOLOv5s, the inference speed is 0.47 times greater than that of YOLOv5s, and the model weight is 2.34 times less than that of YOLOv5s. According to the obtained results, the model proposed in the current work shows high accuracy and inference speed and acquires the automatic detection of cow mounting behavior in dense scenes, which would be beneficial for the all-weather real-time monitoring of multi-channel cameras in large cattle farms. [ABSTRACT FROM AUTHOR]

• A machine vision method was developed to monitor fan operations in animal houses. • Aggregability and discriminability were found for the displacement matrix of pixels. • Proper resolution and threshold were recommended in using the proposed method. • Fan running state was 100% identified with the error of 1.84% for airflow rate. Real-time monitoring of fan operation is essential for supervising and regulating the airflow rate in a mechanical ventilated animal house. This study proposed a real-time method based on image processing and mathematical modelling to monitor the running state and airflow rate of exhaust fans to ensure the adequate ventilation in a livestock house. Videos and actual airflow rates of a running fan were collected at different operating levels (15, 25, 35, and 45 Hz). The Hough transformation was used to locate the fan in the image, and the dense optical flow was used to calculate the displacement of pixels during fan operation. It was found that the vector sum of the displacements for all pixels in the image of a running fan tends to be zero and the variances of the displacements were discriminable at different operation levels, which can be potentially used to monitor the running state and airflow rate of an exhaust fan. Running state (yes or no) of the fan was identified according to the proportion of moving pixels in the target area and its airflow rate can be estimated by curve fitting between the variance of pixel displacement and measured airflow rates at different operating levels. Buffer time window was used for smoothing to increase the accuracy and stability of fan operation monitoring. Using the proposed method, running state of the fan could be 100 % identified and all fans in sight of the camera could be monitored at the same time. In estimating airflow rate, average absolute percentage error of the propose method was 1.84 % (115.30 m3/h) and it reaches the detection limitation when the operating levels was above 72 % of the maximum airflow rate (50 Hz, 18,000 m3/h) due to the time gap between fan running and the frame of imaging. Furthermore, results showed that a minimum resolution of 1280 × 720 was required when using this method to estimate airflow rate of a fan and the higher resolution of images could improve the estimation accuracy. [ABSTRACT FROM AUTHOR]

[Display omitted] • Innovative autonomous system for precise dead hen detection in caged layer houses. • Novel RTMDet-adapted model for accurate real-time dead hen identification. • Advanced parameter strategy for enhanced real-time dead hen detection accuracy. Daily inspections of individual laying hens in large-scale egg farms are both labor-intensive and time-consuming, requiring farm staff to manually check each caged hen and promptly remove any deceased birds to prevent the spread of disease within the battery cages. To streamline this process, a specialized robot has been developed to enhance inspection efficiency, reduce manual labor, and enable rapid identification of dead hens. This inspection robot integrates cutting-edge technologies such as deep learning for real-time detection and identification, QR code-based positioning for precise localization, and autonomous navigation for seamless movement through the farm. It automates the otherwise tedious inspection process by visualizing and pinpointing the location of dead hens within the cages. In experimental tests, the robot achieved a detection accuracy of 90.61 % by incorporating a supplementary lighting system, setting an inspection speed of 9 m per minute, and fine-tuning the inspection algorithm with a probability value parameter of 0.48 and an area ratio parameter of 0.05. Additionally, the robot demonstrated a low false detection rate of 0.14 % and a minimal obvious false detection rate of 0.06 %. Compared to traditional manual inspection methods, this robotic system not only automates the task but also significantly reduces labor requirements and improves the overall management efficiency of large-scale egg farms. With its high accuracy and speed, the robot presents a viable solution for modern poultry operations, ensuring timely removal of dead hens and contributing to better farm hygiene and animal welfare. [ABSTRACT FROM AUTHOR]

[Display omitted] • Innovative autonomous system for precise dead hen detection in caged layer houses. • Novel RTMDet-adapted model for accurate real-time dead hen identification. • Advanced parameter strategy for enhanced real-time dead hen detection accuracy. Daily inspections of individual laying hens in large-scale egg farms are both labor-intensive and time-consuming, requiring farm staff to manually check each caged hen and promptly remove any deceased birds to prevent the spread of disease within the battery cages. To streamline this process, a specialized robot has been developed to enhance inspection efficiency, reduce manual labor, and enable rapid identification of dead hens. This inspection robot integrates cutting-edge technologies such as deep learning for real-time detection and identification, QR code-based positioning for precise localization, and autonomous navigation for seamless movement through the farm. It automates the otherwise tedious inspection process by visualizing and pinpointing the location of dead hens within the cages. In experimental tests, the robot achieved a detection accuracy of 90.61 % by incorporating a supplementary lighting system, setting an inspection speed of 9 m per minute, and fine-tuning the inspection algorithm with a probability value parameter of 0.48 and an area ratio parameter of 0.05. Additionally, the robot demonstrated a low false detection rate of 0.14 % and a minimal obvious false detection rate of 0.06 %. Compared to traditional manual inspection methods, this robotic system not only automates the task but also significantly reduces labor requirements and improves the overall management efficiency of large-scale egg farms. With its high accuracy and speed, the robot presents a viable solution for modern poultry operations, ensuring timely removal of dead hens and contributing to better farm hygiene and animal welfare. [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]

Cow recognition forms the foundation of smart livestock management. Current closed-set cow recognition models can exclusively identify cows within their training set and are ill-equipped to recognize other subjects in the open world. Additionally, there is a dearth of comprehensive research on open-set cow recognition. Existing open-set recognition models not only exhibit suboptimal accuracy but also impose a substantial burden on hardware resources due to their extensive parameterization. These challenges hinder the practical deployment of cow recognition models. This study introduces an innovative open-set metric learning-based recognition framework for cow-back patterns, aiming to rectify a significant limitation of individual cow recognition models, which is their inability to identify cows not included in the training set. The proposed framework combines various loss functions, metric methods, and backbone networks. This integration enables the open-set recognition of cow back pattern images, thereby facilitating the identification of cows previously unrecognized by the model. To mitigate hardware resource consumption, we have devised a novel ultra-lightweight backbone network known as LightCowsNet. This network draws inspiration from existing lightweight backbone networks such as MobileFaceNet, MobileViT, and EfficientNetV2. Its purpose is to extract features from cow back pattern images efficiently. LightCowsNet employs attention mechanisms, inverted residual structures, and depthwise separable convolutions. LightCowsNet first uses attention mechanisms, inverted residual structures, and depthwise separable convolutions to design multiple new feature extraction modules. These modules, which have different structures, are subsequently placed to extract different levels of image information, which enables complete extraction of the textural information of the cows' back patterns. The present study reorganized the Cows2021 dataset to render it suitable for open-set recognition of cow-back patterns, with the test set comprising image pairs. The results revealed that using A-SoftMax as the loss function and Euclidean distance as the metric method enabled LightCowsNet to achieve the highest accuracy of 94.26 %, with a model weight space occupation of only 4.06 MB. Compared to MobileFaceNet, MobileViT, and EfficientNetV2, LightCowsNet achieved 6.24 %, 8.82 %, and 2.3 % greater accuracy on the test set, respectively. Its weight also decreased by 0.71 MB, 0.24 MB, and 4.3 MB, respectively. These results demonstrate that the proposed model achieved both accuracy improvement and model lightweightness, and these findings could serve as reference solutions for intelligent farming on cow farms. [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]

Open lots are commonly used to provide outdoor free ranging spaces for dairy cows in many countries such as China and the USA. For this type of operation, the manure characteristics, floor types and environmental conditions, which potentially affect gaseous emissions from manure, are quite different from those of indoor systems. The estimation of NH 3 emissions from manure depends greatly on the mass transfer coefficient, an empirical constant determined by environmental factors in the process-based emission model. Using a scale model, this study measured ammonia emissions to be expected from dairy open lots, based on different combinations of different surface air velocities (0.6–2.2 m s−1), air temperatures (15–35 °C) and relative humidity values (20–60%). Based on the two-film theory, the overall mass transfer coefficient (K OL) of NH 3 was calculated from directly measured NH 3 emission rates and the ammonium nitrogen content, temperature, and pH of manure. As an indicator of the driving potential of gaseous volatilisation, the vapour pressure deficit (VPD) was estimated using air temperature and relative humidity. As the VPD and surface air velocity increased, the measured NH 3 emission rates ranged from 1.35 to 19.18 mg kg−1 h−1, and the estimated K OL of NH 3 from dairy open lots was within the range of 1.48 × 10−6 – 7.86 × 10−6 m s−1. Using the air velocity and VPD as variables, a non-linear empirical model was developed to estimate the K OL for NH 3 emissions from manure on dairy open lots (R 2 = 0.69). The normalised mean error of the model was approximately 19.4%. • Overall mass transfer coefficient (K OL) was estimated based on two-film theory. • K OL of NH 3 emission was in the range of 1.48–7.86 10−6 m s−1 from dairy open lots. • Air vapour pressure deficit (VPD) was positively related to NH 3 emission and K OL. • A nonlinear model was obtained to estimate K OL using air velocity and VPD. [ABSTRACT FROM AUTHOR]

Gut microbial dysbiosis has always served as a potential factor in the occurrence and development of liver fibrosis. Liver and gut microflora can regulate each other through the gut-liver axis. In this study, the 16S rRNA and RNA-seq were chosen to sequence gut microbiota alteration and liver differentially expressed genes (DEGs) in carbon tetrachloride (CCl4) included-liver fibrosis mice, and analyze the correlations between gut microbiota constituents and DEGs. Results indicated that, CCl4 significantly increased the abundance of Desulfobactera in the phylum level, destroyed gut microbiota balance in the genus levels, especially Enterorhabdus and Desulfovibrio. Through analysis, 1416 genes were found differentially expressed in mice liver tissue in the CCl4 Group, compared with the Control Group; and the DEGs were mainly involved in the lipid metabolic process and immune system process. The correlation analysis revealed that the relative abundance of microbiota phylum (Desulfobactera) and genus (Enterorhabdus and Desulfovibrio) was negatively correlated with the metabolism related genes, while positively correlated with immune-related genes and the genes enriched in PI3K-Akt signaling pathway. To sum up, CCl4 can partially regulate gene expression in metabolism, immune response and the PI3K/Akt pathway, and further maintain the stability of the gut environment in liver fibrosis mice. [ABSTRACT FROM AUTHOR]

• A novel modular segmentation scheme for beef cattle point clouds was proposed. • An automatic bidirectional tomographic slice algorithm was developed. • The segmentation algorithm showed high operating accuracy of 89–95%. As the world population grows and the increase in food consumption creates unprecedented pressure on beef producers, innovative technologies for sustainable and efficient beef production is quintessential. Therefore, a significant effort is ongoing on developing digital precision technologies for continuous beef cattle monitoring and accurate cattle growth estimation. One such technology relies on 3D visual sensor technologies. 3D point cloud data acquired with 3D cameras can be processed to evaluate the livestock's growth condition with minimal human intervention. Essentially, the automatic measurement of animal body dimensions highly depends on the location accuracy of the key points or regions in point clouds. However, the location of specific points and regions requires extra assistance and usually leads to semi-automatic and low-accuracy solutions at best. To address these issues, we demonstrated a novel strategy for partial segmentation of beef cattle point clouds, based on the Bidirectional Tomographic Slice Segmentation (BTSS) algorithm. The cattle point clouds can be successfully segmented into the head and neck, front trunk, middle trunk, back trunk, lower leg, and hip and tail regions with high accuracies of 89%, 91%, 94%, 95%, 92%, and 95%, respectively. Consequently, the segmentation completion rate is 96%, and the average segmentation accuracy reaches 92.8%. When compared to traditional approaches, our method efficiently extracted abundant body characteristics from the beef cattle point cloud. Moreover, the proposed algorithm exhibited generalization capabilities to segment the point clouds of other cloven-hoofed livestock species. The accurate localization of key regions enabled body dimensions measurements and non-contact weighing, which can provide a solid support for breeding needs such as health evaluation, production performance measurement, and genetic breeding evaluation. [ABSTRACT FROM AUTHOR]

• We designed a real-time point cloud collection system for beef cattle with five depth cameras on a gantry structure. • A method of 3D reconstruction of point clouds was proposed by developing the algorithms of automatic registration and principal direction recognition. • We predicted the quality of collected point clouds with the light intensity attenuation coefficient. Body size, weight, and body condition score parameters are key indicators for monitoring cattle growth and they can be utilized to predict beef cattle yield and evaluate economic traits. However, it is easy to lay intense stress on cattle while measuring livestock's body size manually, also along with giving negative effects on their feeding and weight gain. To resolve this problem, we design a real-time point cloud collection system for beef cattle with five depth cameras on a gantry structure. We developed point cloud preprocessing, registration, and 3D reconstruction algorithms, and quantitatively estimated the influence of light intensity during point cloud collection. The algorithms perform point cloud filtering, registration, segmentation, down-sampling, 3D reconstruction of the global point cloud, and target recognition. The maximum uncertainty of the calculated body width and length is 20 mm, and the acquisition time is within 0.08 s. We established a real-time system for 3D cattle point cloud- collection, which involves no stress on cattle when measuring. The point cloud collected by the system can provide technical support for the automatic extraction of key features during livestock body measurements. [ABSTRACT FROM AUTHOR]

Alumina micro-channel parts were produced by micro powder injection molding. The debound part was rapidly densified by pressuresless spark plasma sintering. Rapid densification of the micro-channel part proved to be feasible. Good shape retention and high densification were achieved for the final micro-channel part. The microstructural analysis revealed that fine-grained microstructures were obtained at the sintering temperatures of 1250–1300°C. The nanoindentation tests showed that the nanohardness and Young’s modulus were dependent on the porosity and grain size. [ABSTRACT FROM AUTHOR]