Dietary Fiber-Derived Microbial Butyrate Suppresses ILC2-Dependent Airway Inflammation in COPD.

Group 2 innate lymphoid cells (ILC2) strongly modulate COPD pathogenesis. However, the significance of microbiota in ILC2s remains unelucidated. Herein, we investigated the immunomodulatory role of short-chain fatty acids (SCFAs) in regulating ILC2-associated airway inflammation and explores its associated mechanism in COPD. In particular, we assessed the SCFA-mediated regulation of survival, proliferation, and cytokine production in lung sorted ILC2s. To elucidate butyrate action in ILC2-driven inflammatory response in COPD models, we administered butyrate to BALB/c mice via drinking water. We revealed that SCFAs, especially butyrate, derived from dietary fiber fermentation by gut microbiota inhibited pulmonary ILC2 functions and suppressed both IL-13 and IL-5 synthesis by murine ILC2s. Using in vivo and in vitro experimentation, we validated that butyrate significantly ameliorated ILC2-induced inflammation. We further demonstrated that butyrate suppressed ILC2 proliferation and GATA3 expression. Additionally, butyrate potentially utilized histone deacetylase (HDAC) inhibition to enhance NFIL3 promoter acetylation, thereby augmenting its expression, which eventually inhibited cytokine production in ILC2s. Taken together, the aforementioned evidences demonstrated a previously unrecognized role of microbial-derived SCFAs on pulmonary ILC2s in COPD. Moreover, our evidences suggest that metabolomics and gut microbiota modulation may prevent lung inflammation of COPD.

Automatic Monitoring Methods for Greenhouse and Hazardous Gases Emitted from Ruminant Production Systems: A Review.

The research on automatic monitoring methods for greenhouse gases and hazardous gas emissions is currently a focal point in the fields of environmental science and climatology. Until 2023, the amount of greenhouse gases emitted by the livestock sector accounts for about 11-17% of total global emissions, with enteric fermentation in ruminants being the main source of the gases. With the escalating problem of global climate change, accurate and effective monitoring of gas emissions has become a top priority. Presently, the determination of gas emission indices relies on specialized instrumentation such as breathing chambers, greenfeed systems, methane laser detectors, etc., each characterized by distinct principles, applicability, and accuracy levels. This paper first explains the mechanisms and effects of gas production by ruminant production systems, focusing on the monitoring methods, principles, advantages, and disadvantages of monitoring gas concentrations, and a summary of existing methods reveals their shortcomings, such as limited applicability, low accuracy, and high cost. In response to the current challenges in the field of equipment for monitoring greenhouse and hazardous gas emissions from ruminant production systems, this paper outlines future perspectives with the aim of developing more efficient, user-friendly, and cost-effective monitoring instruments.

Identification and role of differentially expressed genes/proteins between pulmonary tuberculosis patients and controls across lung tissues and blood samples.

BACKGROUND: Differentially expressed genes/proteins (DEGs/DEPs) play critical roles in pulmonary tuberculosis (PTB) diagnosis and treatment. However, there is a scarcity of reports on DEGs/DEPs in lung tissues and blood samples in PTB patients. OBJECTIVE: We aim to identify the DEGs/DEPs in lung tissues and blood samples of PTB patients and investigate their roles in PTB. MATERIALS AND METHODS: The lung granulomas and normal tissues were collected from PTB patients for proteomic and transcriptomic analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses annotated the functions of DEGs/DEPs. The GSE107994 data set was downloaded to identify the DEGs/DEPs in peripheral blood. The common DEGs and DEPs were identified. A nomogram was established. Pearson correlation analysis was conducted. RESULTS: Eighty-three DEGs/DEPs were identified. These DEGs/DEPs were mainly enriched in the movement of cell or subcellular components, regulation of cellular component biogenesis, and actin filament-based process as well as in the pathways of inositol phosphate metabolism, adherens junction, phosphatidylinositol signaling system, leukocyte transendothelial migration, regulation of actin cytoskeleton, and tight junction. There were eight common DEGs/DEPs (TYMP, LAP3, ADGRL2, SIL1, LMO7, SULF 1, ANXA3, and PACSIN3) between the lung tissues and blood samples. They were effective in predicting tuberculosis. Moreover, the activated dendritic cells, macrophages, monocytes, neutrophils, and regulatory T cells were significantly positively correlated with TYMP (r > .50), LAP3 (r > .50), SIL1 (r > .50), ANXA3 (r > .5), and PACSIN3 (r < .50), while negatively correlated with LMO7 (r < -0.50) (p < .05). ADGRL2 and SULF1 did not have a significant correlation (p > .05). LIMITATIONS: The sample size was small. CONCLUSIONS: Eight common DEGs/DEPs of lung tissues and blood samples were identified. They were correlated with immune cells and demonstrated predictive value for PTB. Our data may facilitate the diagnosis and treatment of PTB.

A Clean and Health-Care-Focused Way to Reduce Indoor Airborne Bacteria in Calf House with Long-Wave Ultraviolet.

Long-term exposure to a relatively high concentration of airborne bacteria emitted from intensive livestock houses could potentially threaten the health and welfare of animals and workers. There is a dual effect of air sterilization and promotion of vitamin D synthesis for the specific bands of ultraviolet light. This study investigated the potential use of A-band ultraviolet (UVA) tubes as a clean and safe way of reducing airborne bacteria and improving calf health. The composition and emission characteristics of airborne bacteria were investigated and used to determine the correct operating regime of UVA tubes in calf houses. Intermittent exceedances of indoor airborne bacteria were observed in closed calf houses. The measured emission intensity of airborne bacteria was 1.13 ± 0.09 × 107 CFU h-1 per calf. Proteobacteria were the dominant microbial species in the air inside and outside calf houses. After UVA radiation, the indoor culturable airborne bacteria decreased in all particle size ranges of the Anderson sampler, and it showed the highest reduction rate in the size range of 3.3-4.7 µm. The results of this study would enrich the knowledge of the source characteristics of the airborne bacteria in intensive livestock farming and contribute to the environmental control of cattle in intensive livestock production.

ILC2s induce Treg but not Th2-type immunity through IL-33/ST2 pathway in pulmonary tuberculosis.

INTRODUCTION: We investigated the function of type 2 innate lymphoid cells (ILC2s) and IL-33 in pulmonary tuberculosis (PTB). METHODOLOGY: Peripheral blood samples were collected from PTB patients and healthy controls. The cytometric bead array was used to detect plasma IL-33, TGF-ß, IL-4, IL-5, IL-6, IL-10, IL-13, and soluble ST2 (sST2). ILC2s, Th2, and Treg cells were detected with flow cytometry. Quantitative real-time PCR was used to measure mRNA levels. ILC2s were co-cultured with peripheral blood mononuclear cells and then intervened with IL-33 or anti-ST2 antibody + IL-33 in vitro. IL-4, IL-6, IL-5, IL-10, IL-13, and TGF-ß levels were measured by enzyme-linked immunosorbent assay. RESULTS: Compared with healthy controls, the levels of IL-33, sST2, TGF-ß, IL-10, and IL-6 in the plasma of PTB patients were significantly higher. No significant difference was found in the plasma IL-4, IL-5, and IL-13 levels. Patients with PTB had significantly increased ILC2s proportion and mRNA levels of RAR-related orphan receptor α and GATA binding protein 3. After 48 h of IL-33 stimulation in vitro, Treg cell proportion significantly increased and the IL-10 level was significantly elevated. Treatment with anti-ST2 abolished these effects. No significant difference was found in cytokines of IL-4, IL-6, IL-5, IL-13, and TGF-ß, or Th2 cells before and after IL-33 treatment. ILC2s proportion in peripheral blood was increased and plasma IL-33 was upregulated in PTB patients. CONCLUSIONS: IL-33 may promote the growth of ILC2s and the production of Treg-related cell cytokines, but not Th2-related cell cytokines, to participate in immune response to PTB.

Medical ozone alleviates acute lung injury by enhancing phagocytosis targeting NETs via AMPK/SR-A1 axis.

Acute lung injury (ALI) linked to sepsis has a high mortality rate, with limited treatment options available. In recent studies, medical ozone has shown promising results in alleviating inflammation and infection. Here, we aimed to evaluate the therapeutic potential of medical ozone in sepsis-induced ALI using a mouse model, measuring behavioral assessments, lung function, and blood flow. Western blot was used to quantify the levels of protein. In vitro, experiments on BMDM cells examine the impact of AMPK inhibitors and agonists on phagocytic activity. Results indicate that medical ozone can enhance the survival rate, ameliorate lung injury, and improve lung function and limb microcirculation in mice with ALI. Notably, it inhibits NETs formation, a crucial player in ALI development. Medical ozone also counteracts elevated TF, MMP-9, and IL-1ß levels. In ALI mice, the effects of ozone are nullified and BMDMs exhibit impaired engulfment of NETs following Sr-a1 knockout. Under normal physiological conditions, the use of an AMPK antagonist produces similar effects to Sr-a1 knockout, significantly inhibiting the phagocytosis of NETs by BMDMs. On the contrary, AMPK agonists enhance this phagocytic process. In conclusion, medical ozone can alleviate sepsis-induced lung injury via the AMPK/SR-A1 pathway, thereby enhancing phagocytosis of NETs by macrophages.

Multi-Target Feeding-Behavior Recognition Method for Cows Based on Improved RefineMask.

Within the current process of large-scale dairy-cattle breeding, to address the problems of low recognition-accuracy and significant recognition-error associated with existing visual methods, we propose a method for recognizing the feeding behavior of dairy cows, one based on an improved RefineMask instance-segmentation model, and using high-quality detection and segmentation results to realize the recognition of the feeding behavior of dairy cows. Firstly, the input features are better extracted by incorporating the convolutional block attention module into the residual module of the feature extraction network. Secondly, an efficient channel attention module is incorporated into the neck design to achieve efficient integration of feature extraction while avoiding the surge of parameter volume computation. Subsequently, the GIoU loss function is used to increase the area of the prediction frame to optimize the convergence speed of the loss function, thus improving the regression accuracy. Finally, the logic of using mask information to recognize foraging behavior was designed, and the accurate recognition of foraging behavior was achieved according to the segmentation results of the model. We constructed, trained, and tested a cow dataset consisting of 1000 images from 50 different individual cows at peak feeding times. The method's effectiveness, robustness, and accuracy were verified by comparing it with example segmentation algorithms such as MSRCNN, Point_Rend, Cascade_Mask, and ConvNet_V2. The experimental results show that the accuracy of the improved RefineMask algorithm in recognizing the bounding box and accurately determining the segmentation mask is 98.3%, which is higher than that of the benchmark model by 0.7 percentage points; for this, the model parameter count size was 49.96 M, which meets the practical needs of local deployment. In addition, the technologies under study performed well in a variety of scenarios and adapted to various light environments; this research can provide technical support for the analysis of the relationship between cow feeding behavior and feed intake during peak feeding periods.

Construction of ceRNA regulatory networks for active pulmonary tuberculosis.

Delayed diagnosis in patients with pulmonary tuberculosis (PTB) often leads to serious public health problems. High throughput sequencing was used to determine the expression levels of lncRNAs, mRNAs, and miRNAs in the lesions and adjacent health lung tissues of patients with PTB. Their differential expression profiles between the two groups were compared, and 146 DElncRs, 447 DEmRs, and 29 DEmiRs were obtained between lesions and adjacent health tissues in patients with PTB. Enrichment analysis for mRNAs showed that they were mainly involved in Th1, Th2, and Th17 cell differentiation. The lncRNAs, mRNAs with target relationship with miRNAs were predicted respectively, and correlation analysis was performed. The ceRNA regulatory network was obtained by comparing with the differentially expressed transcripts (DElncRs, DEmRs, DEmiRs), then 2 lncRNAs mediated ceRNA networks were established. The expression of genes within the network was verified by quantitative real-time PCR (qRT-PCR). Flow cytometric analysis revealed that the proportion of Th1 cells and Th17 cells was lower in PTB than in controls, while the proportion of Th2 cells increased. Our results provide rich transcriptome data for a deeper investigation of PTB. The ceRNA regulatory network we obtained may be instructive for the diagnosis and treatment of PTB.

ATF3 is a neuron-specific biomarker for spinal cord injury and ischaemic stroke.

BACKGROUND: Although many molecules have been investigated as biomarkers for spinal cord injury (SCI) or ischemic stroke, none of them are specifically induced in central nervous system (CNS) neurons following injuries with low baseline expression. However, neuronal injury constitutes a major pathology associated with SCI or stroke and strongly correlates with neurological outcomes. Biomarkers characterized by low baseline expression and specific induction in neurons post-injury are likely to better correlate with injury severity and recovery, demonstrating higher sensitivity and specificity for CNS injuries compared to non-neuronal markers or pan-neuronal markers with constitutive expressions. METHODS: In animal studies, young adult wildtype and global Atf3 knockout mice underwent unilateral cervical 5 (C5) SCI or permanent distal middle cerebral artery occlusion (pMCAO). Gene expression was assessed using RNA-sequencing and qRT-PCR, while protein expression was detected through immunostaining. Serum ATF3 levels in animal models and clinical human samples were measured using commercially available enzyme-linked immune-sorbent assay (ELISA) kits. RESULTS: Activating transcription factor 3 (ATF3), a molecular marker for injured dorsal root ganglion sensory neurons in the peripheral nervous system, was not expressed in spinal cord or cortex of naïve mice but was induced specifically in neurons of the spinal cord or cortex within 1 day after SCI or ischemic stroke, respectively. Additionally, ATF3 protein levels in mouse blood significantly increased 1 day after SCI or ischemic stroke. Importantly, ATF3 protein levels in human serum were elevated in clinical patients within 24 hours after SCI or ischemic stroke. Moreover, Atf3 knockout mice, compared to the wildtype mice, exhibited worse neurological outcomes and larger damage regions after SCI or ischemic stroke, indicating that ATF3 has a neuroprotective function. CONCLUSIONS: ATF3 is an easily measurable, neuron-specific biomarker for clinical SCI and ischemic stroke, with neuroprotective properties. HIGHLIGHTS: ATF3 was induced specifically in neurons of the spinal cord or cortex within 1 day after SCI or ischemic stroke, respectively. Serum ATF3 protein levels are elevated in clinical patients within 24 hours after SCI or ischemic stroke. ATF3 exhibits neuroprotective properties, as evidenced by the worse neurological outcomes and larger damage regions observed in Atf3 knockout mice compared to wildtype mice following SCI or ischemic stroke.

The Heptazine-Based Materials through Intrinsically Modification for the Cycloaddition of CO2 and Bisepoxides.

For the efficient utilization of CO2 into valuable product, the attractive carbon nitride catalysts have been widely studied. In this work, heptazine-related materials with varying degree of polymerization were designed by an intrinsically modification strategy and employed in the cycloaddition of CO2 with the bisepoxide 1, 4-butanediol diglycidyl ether (BDODGE). We initially figured out that the sample prepared at 450 °C contained more melem hydrate, exhibiting the best performance. The epoxides conversion and corresponding cyclic carbonates selectivity could achieve 93.1 % and 99.3 % at 140 °C for 20 h without any cocatalyst and solvent, respectively. Results of the catalytic tests suggested that the high catalytic activity was dependent on big size porous structure and the synergetic effect of active amino groups and -OH groups. The role of water in maintaining the specific structure and providing active site has been proved. Moreover, the CN-450-W catalyst exhibited outstanding recycling stability. And finally, a plausible reaction mechanism was proposed.

Characterization of a novel VenusX orthogonal dual-layer multileaf collimator.

PURPOSE: To investigate and characterize the performance of a novel orthogonal dual-layer alpha multileaf collimator (αMLC) mounted on the LinaTech VenusX linac. METHODS: We evaluated leaf positioning accuracy and reproducibility using an electronic portal imaging device through the picket fence test. The average, interleaf, intraleaf, and leaf tip transmissions of the single and dual layers were measured using an ionization chamber. Square and rhombus fields were used to evaluate the leaf penumbra of αMLC. To investigate the advantages of the orthogonal dual-layer multileaf collimator (MLC) in field shaping, right triangular and circular pattern fields were formed using both the dual layers and single layers of the αMLC. RESULTS: The average maximum positioning deviations of the upper and lower αMLC over 1 year were 0.76 ± 0.09 mm and 0.62 ± 0.07 mm, respectively. The average transmissions were 1.87%, 1.83%, and 0.03% for the upper-, lower- and dual-layer αMLC, respectively. The maximum interleaf transmissions of the lower- and dual-layer were 2.43% and 0.17%, respectively. The leaf tip transmissions were 9.34% and 0.25%, respectively. The penumbra of the square field was 6.2 mm in the X direction and 8.0 mm in the Y direction. The average penumbras of the rhombus fields with side lengths of 5 and 10 cm were 3.6 and 4.9 mm, respectively. For the right triangular and circular fields, the fields shaped by the dual-layer leaves were much closer to the set field than those shaped by single-layer leaves. The dose undulation amplitude of the 50% isodose lines and leaf stepping angle change of the dual-layer leaves were smaller than those of the single-layer leaves. CONCLUSIONS: The αMLC benefits from its orthogonal dual-layer design. Leaf transmission, dose undulations at the field edge, and MLC field dependence of the leaf stepping angle of the dual-layer αMLC were remarkably reduced.

Computer-Vision-Based Sensing Technologies for Livestock Body Dimension Measurement: A Survey.

Livestock's live body dimensions are a pivotal indicator of economic output. Manual measurement is labor-intensive and time-consuming, often eliciting stress responses in the livestock. With the advancement of computer technology, the techniques for livestock live body dimension measurement have progressed rapidly, yielding significant research achievements. This paper presents a comprehensive review of the recent advancements in livestock live body dimension measurement, emphasizing the crucial role of computer-vision-based sensors. The discussion covers three main aspects: sensing data acquisition, sensing data processing, and sensing data analysis. The common techniques and measurement procedures in, and the current research status of, live body dimension measurement are introduced, along with a comparative analysis of their respective merits and drawbacks. Livestock data acquisition is the initial phase of live body dimension measurement, where sensors are employed as data collection equipment to obtain information conducive to precise measurements. Subsequently, the acquired data undergo processing, leveraging techniques such as 3D vision technology, computer graphics, image processing, and deep learning to calculate the measurements accurately. Lastly, this paper addresses the existing challenges within the domain of livestock live body dimension measurement in the livestock industry, highlighting the potential contributions of computer-vision-based sensors. Moreover, it predicts the potential development trends in the realm of high-throughput live body dimension measurement techniques for livestock.

Study on a Pig Vocalization Classification Method Based on Multi-Feature Fusion.

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.

Research on the Inhibitory Effect of Doxorubicin-loaded Liposomes Targeting GFAP for Glioma Cells.

BACKGROUND: Glioma is the most common and devastating brain tumor. In recent years, doxorubicin (DOX) is one of the drugs used in the treatment of gliomas, but it has side effects and poor clinical outcomes. Therefore, the delivery of drugs to the tumor site by targeted transport is a new approach to tumor treatment. OBJECTIVE: This study focuses on the anti-tumor effects of GFAP-modified drug-carrying liposomes loaded with DOX (GFAP-DOX-LPs) on gliomas. METHODS: GFAP-DOX-LPs were prepared by solvent evaporation method. After characterization analysis of GFAP-DOX-LPs, the encapsulation efficiency, the drug loading capacity and in vitro release performance were determined. Then, the MTT method was used to investigate the cytotoxicity and proliferative behavior of U251 and U87 cell lines. After that, flow cytometry was used to investigate the effect of the drug administration group on tumor cell apoptosis. Eventually, the anti-tumor activity was tested in vivo. RESULTS: The average particle size of GFAP-DOX-LPs was determined to be 116.3 ± 6.2 nm, and the average potential was displayed as 22.8 ± 7.2 mv. Besides, the morphology of the particle indicated a spherical shape. The encapsulation rate and drug loading were calculated and determined, which were 91.84 ± 0.41% and 9.27 ± 0.55%. In an acidic medium, the DOX release rate reached about 87%. GFAP-DOX-LPs could target glioma cells with low cytotoxicity and inhibit glioma cell proliferation with high efficiency, resulting in promoting apoptosis. The anti-tumor effect of GFAP-DOX-LPs was significantly enhanced. At the same time, the number of GFAPpositive cells in tumor tissues was significantly lower after treatment. Therefore, the overall survival time could be significantly prolonged. CONCLUSION: The prepared GFAP-DOX-LPs had good targeting and glioma cell inhibition ability. This demonstrated the promising application of the prepared liposomes in tumor targeting, especially in the field of targeted drug delivery for the treatment of brain tumor.

The egg ribonuclease SjCP1412 accelerates liver fibrosis caused by Schistosoma japonicum infection involving damage-associated molecular patterns (DAMPs).

Schistosomiasis, a parasite infectious disease caused by Schistosoma japonicum, often leads to egg granuloma and fibrosis due to the inflammatory reaction triggered by egg antigens released in the host liver. This study focuses on the role of the egg antigens CP1412 protein of S. japonicum (SjCP1412) with RNase activity in promoting liver fibrosis. In this study, the recombinant egg ribonuclease SjCP1412, which had RNase activity, was successfully prepared. By analysing the serum of the population, it has been proven that the anti-SjCP1412 IgG in the serum of patients with advanced schistosomiasis was moderately correlated with liver fibrosis, and SjCP1412 may be an important antigen associated with liver fibrosis in schistosomiasis. In vitro, the rSjCP1412 protein induced the human liver cancer cell line Hep G2 and liver sinusoidal endothelial cells apoptosis and necrosis and the release of proinflammatory damage-associated molecular patterns (DAMPs). In mice infected with schistosomes, rSjCP1412 immunization or antibody neutralization of SjCP1412 activity significantly reduced cell apoptosis and necroptosis in liver tissue, thereby reducing inflammation and liver fibrosis. In summary, the SjCP1412 protein plays a crucial role in promoting liver fibrosis during schistosomiasis through mediating the liver cells apoptosis and necroptosis to release DAMPs inducing an inflammatory reaction. Blocking SjCP1412 activity could inhibit its proapoptotic and necrotic effects and alleviate hepatic fibrosis. These findings suggest that SjCP1412 may be served as a promising drug target for managing liver fibrosis in schistosomiasis japonica.

Species identification and strain discrimination of fermentation yeasts Saccharomyces cerevisiae and Saccharomyces uvarum using Raman spectroscopy and convolutional neural networks.

IMPORTANCE: The use of S. cerevisiae and S. uvarum yeast starter cultures is a common practice in the alcoholic beverage fermentation industry. As yeast strains from different or the same species have variable fermentation properties, rapid and reliable typing of yeast strains plays an important role in the final quality of the product. In this study, Raman spectroscopy combined with CNN achieved accurate identification of S. cerevisiae and S. uvarum isolates at both the species and strain levels in a rapid, non-destructive, and easy-to-operate manner. This approach can be utilized to test the identity of commercialized dry yeast products and to monitor the diversity of yeast strains during fermentation. It provides great benefits as a high-throughput screening method for agri-food and the alcoholic beverage fermentation industry. This proposed method has the potential to be a powerful tool to discriminate S. cerevisiae and S. uvarum strains in taxonomic, ecological studies and fermentation applications.

Characteristics of Serum Autoantibody Repertoire and Immune Subgroup Variation of Tuberculosis-Associated Obstructive Pulmonary Disease.

Background: Studying the potential etiology and pathogenesis of tuberculosis-associated chronic obstructive pulmonary disease (TOPD) from an autoimmunity perspective may provide insights into peripheral blood autoantibodies and immune cells, as well as their interactions. Methods: This study examined the serum autoantibody repertoire in healthy individuals, patients with chronic obstructive pulmonary disease (COPD), patients with pulmonary tuberculosis (TB), and TOPD patients using the HuProtTM protein chip. Autoantigens in the peripheral blood of TOPD patients were verified using ELISA assay. Various epitopes and immune simulation were predicted using bioinformatic methods. Flow cytometry was employed to detect macrophages(Mφ), T cells, and innate lymphoid cells (ILCs) in the peripheral blood. Results: COPD patients displayed distinct alterations in their IgG and IgM autoantibodies compared to the other groups. GeneOntology (GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG)analyses revealed that these autoantibodies were associated with regulating macrophages, T cells, and B cells. ELISA results confirmed the upregulation of expression of proliferating cell nuclear antigen (PCNA), Mitogen-Activated Protein Kinase 3 antigen (MAPK3), and threonine protein kinase 1 antigen (AKT1) proteins in the peripheral blood of TOPD patients. Bioinformatic analysis predicted multiple potential epitopes in Th, CTL, and B cells. Immune simulation results demonstrated that PCNA, MAPK3, and AKT1 can activate innate and adaptive immune responses and induce the expression of different cytokines, such as IFN-g and IL-2. Furthermore, data obtained from flow cytometry assay revealed an upregulation in the face of Th1 cells in the peripheral blood of TOPD patients. Conclusion: Tuberculosis infection can effectively induce autoimmune responses, contributing to increased expression of Th1 cells and associated cytokines, ultimately leading to immune dysregulation. Furthermore, the accumulation of pulmonary inflammatory response facilitates the progression of TOPD and is helpful for the clinical diagnosis and the development of targeted therapeutic drugs for this disease.

X3DFast model for classifying dairy cow behaviors based on a two-pathway architecture.

Behavior is one of the important factors reflecting the health status of dairy cows, and when dairy cows encounter health problems, they exhibit different behavioral characteristics. Therefore, identifying dairy cow behavior not only helps in assessing their physiological health and disease treatment but also improves cow welfare, which is very important for the development of animal husbandry. The method of relying on human eyes to observe the behavior of dairy cows has problems such as high labor costs, high labor intensity, and high fatigue rates. Therefore, it is necessary to explore more effective technical means to identify cow behaviors more quickly and accurately and improve the intelligence level of dairy cow farming. Automatic recognition of dairy cow behavior has become a key technology for diagnosing dairy cow diseases, improving farm economic benefits and reducing animal elimination rates. Recently, deep learning for automated dairy cow behavior identification has become a research focus. However, in complex farming environments, dairy cow behaviors are characterized by multiscale features due to large scenes and long data collection distances. Traditional behavior recognition models cannot accurately recognize similar behavior features of dairy cows, such as those with similar visual characteristics, i.e., standing and walking. The behavior recognition method based on 3D convolution solves the problem of small visual feature differences in behavior recognition. However, due to the large number of model parameters, long inference time, and simple data background, it cannot meet the demand for real-time recognition of dairy cow behaviors in complex breeding environments. To address this, we developed an effective yet lightweight model for fast and accurate dairy cow behavior feature learning from video data. We focused on four common behaviors: standing, walking, lying, and mounting. We recorded videos of dairy cow behaviors at a dairy farm containing over one hundred cows using surveillance cameras. A robust model was built using a complex background dataset. We proposed a two-pathway X3DFast model based on spatiotemporal behavior features. The X3D and fast pathways were laterally connected to integrate spatial and temporal features. The X3D pathway extracted spatial features. The fast pathway with R(2 + 1)D convolution decomposed spatiotemporal features and transferred effective spatial features to the X3D pathway. An action model further enhanced X3D spatial modeling. Experiments showed that X3DFast achieved 98.49% top-1 accuracy, outperforming similar methods in identifying the four behaviors. The method we proposed can effectively identify similar dairy cow behaviors while improving inference speed, providing technical support for subsequent dairy cow behavior recognition and daily behavior statistics.

A lightweight cow mounting behavior recognition system based on improved YOLOv5s.

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.

Neural regeneration ability of Polypyrrole-Collagen-Quercetin composite in the spinal cord injury.

Spinal cord injury (SCI) is a major clinical problem in young patients. The major hurdle in SCI regeneration is the replacement of lost nerve communication signals due to injury. Here we have prepared a biocompatible electrical conductive composite such as Collagen-Polypyrrole combined with Quercetin (Col-PPy-Qur) composite. The prepared composites are characterized for their chemical functionality and morphology by the FTIR and SEM & TEM analysis, respectively. The Col-PPy-Qur composite observed electrical conductivity at 0.0653 s/cm due to the conductive Polypyrrole polymer present in the composite. The Col-PPy-Qur composite exhibits a mechanical strength of 0.1281 mPa, similar to the native human spinal cord's mechanical strength. In order to explore the regeneration potential, the viability of the composite has been tested with human astrocyte cells (HACs). The Tuj1 and GFAF marker expression was quantized by RT-PCR analysis. Increased Tuj1 and decreased GFAF expression by the Col-PPy-Qur composite indicated the potential differentiation ability of the HACs into neuron cells. The results indicated that the Col-PPy-Qur composite could have good regeneration and differentiation ability, better biocompatibility, and suitable mechanical and conductivity properties. It can act as an excellent strategy for spinal cord regeneration in the nearer future.