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.

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.

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.

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.

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. METHOD: 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.

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.

A Machine Learning Framework Based on Extreme Gradient Boosting to Predict the Occurrence and Development of Infectious Diseases in Laying Hen Farms, Taking H9N2 as an Example.

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.

Exercise-induced endothelial Mecp2 lactylation suppresses atherosclerosis via the Ereg/MAPK signalling pathway.

BACKGROUND AND AIMS: Lactylation, a recently identified post-translational modification (PTM), plays a central role in the regulation of multiple physiological and pathological processes. Exercise is known to provide protection against cardiovascular disease. However, whether exercise-generated lactate changes lactylation and is involved in the exercise-induced attenuation of atherosclerotic cardiovascular disease (ASCVD) remains unclear. The purpose of this study was to investigate the effects and mechanisms of exercise-induced lactylation on ASCVD. METHODS AND RESULTS: Using the high-fat diet-induced apolipoprotein-deficient mouse model of ASCVD, we found that exercise training promoted Mecp2 lysine lactylation (Mecp2k271la); it also decreased the expression of vascular cell adhesion molecule 1 (Vcam-1), intercellular adhesion molecule 1 (Icam-1), monocyte chemoattractant protein 1 (Mcp-1), interleukin (IL)-1ß, IL-6, and increased the level of endothelial nitric oxide synthase (Enos) in the aortic tissue of mice. To explore the underlying mechanisms, mouse aortic endothelial cells (MAECs) were subjected to RNA-sequencing and CHIP-qPCR, which confirmed that Mecp2k271la repressed the expression of epiregulin (Ereg) by binding to its chromatin, demonstrating Ereg as a key downstream molecule for Mecp2k271la. Furthermore, Ereg altered the mitogen-activated protein kinase (MAPK) signalling pathway through regulating the phosphorylation level of epidermal growth factor receptor, thereby affecting the expression of Vcam-1, Icam-1, Mcp-1, IL-1ß, IL-6, and Enos in ECs, which in turn promoted the regression of atherosclerosis. In addition, increasing the level of Mecp2k271la by exogenous lactate administration in vivo also inhibits the expression of Ereg and the MAPK activity in ECs, resulting in repressed atherosclerotic progression. CONCLUSIONS: In summary, this study provides a mechanistic link between exercise and lactylation modification, offering new insight into the anti-atherosclerotic effects of exercise-induced PTM.

Interaction between the PI3K/AKT pathway and mitochondrial autophagy in macrophages and the leukocyte count in rats with LPS-induced pulmonary infection.

This study examined the effects of the PI3K/AKT pathway and mitochondrial autophagy in macrophages and the leukocyte count after pulmonary infection. Sprague‒Dawley rats were subjected to tracheal injection of lipopolysaccharide (LPS) to establish animal models of pulmonary infection. By inhibiting the PI3K/AKT pathway or inhibiting/inducing mitochondrial autophagy in macrophages, the severity of the pulmonary infection and the leukocyte count were altered. The PI3K/AKT inhibition group did not show a significant difference in leukocyte counts compared with the infection model group. Mitochondrial autophagy induction alleviated the pulmonary inflammatory response. The infection model group had significantly higher levels of LC3B, Beclin1, and p-mTOR than the control group. The AKT2 inhibitor group exhibited significantly increased levels of LC3B and Beclin1 compared with the control group (P < 0.05), and the Beclin1 level was significantly higher than that in the infection model group (P < 0.05). Compared with the infection model group, the mitochondrial autophagy inhibitor group exhibited significantly decreased levels of p-AKT2 and p-mTOR, whereas the levels of these proteins were significantly increased in the mitochondrial autophagy inducer group (P < 0.05). PI3K/AKT inhibition promoted mitochondrial autophagy in macrophages. Mitochondrial autophagy induction activated the downstream gene mTOR of the PI3K/AKT pathway, alleviated pulmonary inflammatory reactions, and decreased leukocyte counts.

N6-methyladenosine-methylomic landscape of lung tissues of mice with chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD), a common respiratory disease, can be divided into stable phase and acute exacerbation phase (AECOPD) and is characterized by inflammation and hyper-immunity. Methylation of N6-methyladenosine (m6A) is an epigenetic modification that regulates the expression and functions of genes by influencing post-transcriptional RNA modifications. Its influence on the immune regulation mechanism has attracted great attention. Herein, we present the m6Amethylomic landscape and observe how the methylation of m6A participates in the pathological process of COPD. The m6A modification of 430 genes increased and that of 3995 genes decreased in the lung tissues of mice with stable COPD. The lung tissues of mice with AECOPD exhibited 740 genes with hypermethylated m6A peak and 1373 genes with low m6A peak. These differentially methylated genes participated in signaling pathways related to immune functions. To further clarify the expression levels of differentially methylated genes, RNA immunoprecipitation sequencing (MeRIP-seq) and RNA-sequencing data were jointly analyzed. In the stable COPD group, 119 hypermethylated mRNAs (82 upregulated and 37 downregulated mRNAs) and 867 hypomethylated mRNAs (419 upregulated and 448 downregulated mRNAs) were differentially expressed. In the AECOPD group, 87 hypermethylated mRNAs (71 upregulated and 16 downregulated mRNAs) and 358 hypomethylated mRNAs (115 upregulated and 243 downregulated mRNAs) showed differential expression. Many mRNAs were related to immune function and inflammation. Together, this study provides important evidence on the role of RNA methylation of m6A in COPD.

Dynamic mechanism of eliminating COVID-19 vaccine hesitancy through web search.

This research focuses on the research problem of eliminating COVID-19 vaccine hesitancy through web search. A dynamic model of eliminating COVID-19 vaccine hesitancy through web search is constructed based on the Logistic model, the elimination degree is quantified, the elimination function is defined to analyze the dynamic elimination effect, and the model parameter estimation method is proposed. The numerical solution, process parameters, initial value parameters and stationary point parameters of the model are simulated, respectively, and the mechanism of elimination is deeply analyzed to determine the key time period. Based on the real data of web search and COVID-19 vaccination, data modeling is carried out from two aspects: full sample and segmented sample, and the rationality of the model is verified. On this basis, the model is used to carry out dynamic prediction and verified to have certain medium-term prediction ability. Through this research, the methods of eliminating vaccine hesitancy are enriched, and a new practical idea is provided for eliminating vaccine hesitancy. It also provides a method to predict the quantity of COVID-19 vaccination, provides theoretical guidance for dynamically adjusting the public health policy of the COVID-19, and can provide reference for the vaccination of other vaccines.

Molecular mechanism by which the Notch signaling pathway regulates autophagy in a rat model of pulmonary fibrosis in pigeon breeder's lung.

This study investigated the molecular mechanisms underlying the involvement of the Notch signaling pathway and autophagy in the development of pulmonary fibrosis in pigeon breeder's lung (PBL). Rats were divided into control (Ctrl), PBL model (M), M + D (Notch signaling inhibition), M + W (autophagy inhibition), and M + R (autophagy induction) groups. Lyophilized protein powder from pigeon shedding materials was used as an allergen to construct a fibrotic PBL rat model. The mechanism by which Notch signaling regulated autophagy in the pulmonary fibrosis of PBL was investigated by inhibiting the Notch pathway and interfering with autophagy. Pulmonary interstitial fibrosis was significantly greater in the M group and the M + W group than in the M + D and M + R groups. The expression of α-smooth muscle actin was significantly higher in the M, M + D, and M + W groups than in the Ctrl group (P < 0.05). The expression of the cell autophagy markers Beclin1 and LC3 was lower in the M, M + D, and M + W groups than in the Ctrl group (P < 0.05), whereas Beclin1 and LC3 expressions were higher in the M + D and M + R groups than in the M group. The levels of reactive oxygen species in serum and lung tissues were higher in the M, M + D, M + W, and M + R groups than in the Ctrl group (P < 0.05). The Notch signaling pathway is involved in the pathological process of pulmonary fibrosis in the rat model of PBL by regulating autophagy.

MPs-ACT, an Assay to Evaluate the Procoagulant Activity of Microparticles.

The procoagulant effect of microparticles (MPs) contributes to hypercoagulability-induced thrombosis. We provide preliminary findings of the MPs-Activated Clotting Time (MPs-ACT) assay to determine the procoagulant activity of MPs. MPs-rich plasma was obtained and recalcified. Changes in plasma viscoelasticity were evaluated and the time to the peak viscoelastic changes was defined as the MPs-ACT. MPs concentration was measured by flow cytometry. Coagulation products produced during plasma clotting were identified by fibrin and fibrinopeptide A. MPs were prepared in vitro and added to standard plasma to simulate pathological samples. In addition, reproducibility and sensitivity were evaluated. We confirmed the linear relationship between MPs-ACT and MP concentrations. Dynamic changes in fibrin production were depicted. We simulated the correlation between MPs-ACT and standard plasma containing MPs prepared in vitro. The reproducibility of high-value and low-value samples was 6.0% and 10.8%, respectively. MPs-ACT sensitively detected hypercoagulable samples from patients with pre-eclampsia, hip fractures, and lung tumors. MPs-ACT largely reflects the procoagulant effect of MPs. MPs-ACT sensitively and rapidly detects hypercoagulability with MPs-rich plasma. It may be promising for the diagnosis of hypercoagulable states induced by MPs.

Infrared and Visible Image Fusion Technology and Application: A Review.

The images acquired by a single visible light sensor are very susceptible to light conditions, weather changes, and other factors, while the images acquired by a single infrared light sensor generally have poor resolution, low contrast, low signal-to-noise ratio, and blurred visual effects. The fusion of visible and infrared light can avoid the disadvantages of two single sensors and, in fusing the advantages of both sensors, significantly improve the quality of the images. The fusion of infrared and visible images is widely used in agriculture, industry, medicine, and other fields. In this study, firstly, the architecture of mainstream infrared and visible image fusion technology and application was reviewed; secondly, the application status in robot vision, medical imaging, agricultural remote sensing, and industrial defect detection fields was discussed; thirdly, the evaluation indicators of the main image fusion methods were combined into the subjective evaluation and the objective evaluation, the properties of current mainstream technologies were then specifically analyzed and compared, and the outlook for image fusion was assessed; finally, infrared and visible image fusion was summarized. The results show that the definition and efficiency of the fused infrared and visible image had been improved significantly. However, there were still some problems, such as the poor accuracy of the fused image, and irretrievably lost pixels. There is a need to improve the adaptive design of the traditional algorithm parameters, to combine the innovation of the fusion algorithm and the optimization of the neural network, so as to further improve the image fusion accuracy, reduce noise interference, and improve the real-time performance of the algorithm.