Search Results (7)

Mycobacterium tuberculosis (MTB) can cause serious infectious diseases. MTB is retained in the macrophages of an organism, activating the immune response or evading the immune response through other mechanisms. Mycobacterium smegmatis (M. smeg) has the advantage of high safety and maneuverability as an alternative to MTB. M. smeg has physiological functions similar to those of MTB. It is mainly used to study the molecular mechanism of the interaction between the modified M. smeg carrying MTB-related components and cells. There are few studies on the interaction between the unmodified M. smeg and macrophages. Transcriptomics is an emerging research tool in recent years, which can deeply explore the relevant molecules inside a cell and explore the possible regulatory mechanisms more comprehensively. In this study, we first constructed an in vitro model of M. smeg-infected macrophages, collected RNA extracted from the infected cells, performed transcriptome sequencing using the Illunima platform, and verified the expression levels of the main markers related to phenotypic or functional changes in macrophages by qPCR and ELISA. In this study, through the transcriptomic analysis of M. smeg-infected macrophages, we found that M. smeg can regulate multiple cell signaling pathways in macrophages dominated by immune responses and activate the production of the cytokines IL-6 and TNF-α, which are mainly involved in the immune response in macrophages. This study suggests that M. smeg and MTB have similar physiological functions in activating the immune response of macrophages. Meanwhile, the interaction between M. smeg and macrophages also indicates the primary position and significant role of immune regulation in cellular signaling pathways. Therefore, studying the interaction mechanism between macrophages and M. smeg through transcriptomics is conducive to a comprehensive understanding of the related physiological functions of M. smeg in regulating immune responses or immune escape, providing strong evidence for its use as a model alternative bacteria for MTB in the future research on MTB immunity and related physiological functions. Full article

Extracellular vesicles (EVs) can be distributed in various bodily fluids, such as serum and urine, and play an essential role in immune regulation, substance transport, and other aspects. Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb), which places a tremendous burden on public health prevention and control within society. Researchers are committed to developing various diagnoses and treatment plans to eliminate TB effectively. The results of some studies conducted to date demonstrate that the serum EVs of TB patients, which carry components related to Mtb, can be used as relevant markers for TB detection and improve diagnostic efficiency. However, no relevant reports exist on the particular physiological functions such EVs perform, thus warranting further exploration. In this study, we collected serum EVs from both healthy individuals and TB patients. After identifying the morphology, concentration, and expression of classic markers (CD63, CD81, and CD9) of EVs, we explored their physiological functions at the cellular level and their physiological functions and effects on BCG colonization in the lungs at the mouse level. It was found that EVs were abundant in TB patients and healthy individuals, and the number of CD63 and CD9 markers co-expressed on the surface of serum EVs in healthy individuals was greater than that in TB patients. Serum EVs in patients with TB can stimulate cells to secrete more immune cytokines, such as TNF-α and IL-6, compared with those in healthy individuals; induce an increase in the M1/M2 ratio of macrophages in the peripheral blood mononuclear cells of mice; and inhibit the colonization of Mycobacterium bovis bacillus Calmette Guérin (BCG) in the lungs of mice. In addition, they can inhibit the occurrence of inflammatory responses in the lung tissue of mice. The above results suggest that serum EVs in TB patients may exert their physiological function by regulating immune responses. This finding also indicates that exploring serum EVs in TB patients with regard to their physiological functions shows excellent potential. Full article

Non-destructive monitoring of chlorophyll content through Soil Plant Analysis Development (SPAD) values is essential for precision agriculture in water-limited regions. However, current estimation methods using spectral information alone face significant limitations in sensitivity and transferability under variable irrigation conditions. While integrating canopy structural parameters with spectral data represents a promising solution, systematic investigation of this approach throughout the entire growth cycle of spring wheat under different irrigation regimes remains limited. This study evaluated three machine learning algorithms (Random Forest, Support Vector Regression, and Multi-Layer Perceptron) for SPAD estimation in spring wheat cultivated in the Hetao Irrigation District. Using a split-plot experimental design with two irrigation treatments (conventional: four irrigations; limited: two irrigations) and five nitrogen levels (0–300 kg·ha⁻¹), we analyzed ten vegetation indices derived from Unmanned Aerial Vehicle (UAV) multispectral imagery, with and without Leaf Area Index (LAI) integration, across six growth stages. Results demonstrated that incorporating LAI significantly improved SPAD estimation accuracy across all algorithms, with Random Forest exhibiting the most substantial enhancement (R² increasing from 0.698 to 0.842, +20.6%; RMSE decreasing from 5.025 to 3.640, −27.6%). Notably, LAI contributed more significantly to SPAD estimation under limited irrigation conditions (R² improvement: +17.6%) compared to conventional irrigation (+11.0%), indicating its particular value for chlorophyll monitoring in water-stressed environments. The Green Normalized Difference Vegetation Index (GNDVI) emerged as the most important predictor (importance score: 0.347), followed by LAI (0.213), confirming the complementary nature of spectral and structural information. These findings provide a robust framework for non-destructive SPAD estimation in spring wheat and highlight the importance of integrating canopy structural information with spectral data, particularly in water-limited agricultural systems. Full article

The characteristics and responses of soil bacterial communities and potato productivity to different fertilization treatments in farmlands in the agropastoral zone of Inner Mongolia were investigated. Moreover, the diversity and structure of soil bacterial communities and potato productivity under different fertilization treatments (no fertilization, CK; phosphorus-deficient treatment, NK; conventional fertilization, NPK; and organic–inorganic combination, NPKM) were assessed using Illumina high-throughput sequencing. The results revealed that soil pH, organic matter (SOM), total nitrogen (TN), and total phosphorus (TP) content, and potato productivity were significantly increased under fertilizer treatments (NK, NPK, and NPKM) compared with those under CK, with NPKM treatment having the best enhancement effect. The application of organic fertilizers significantly increased the Shannon, evenness, Chao1, and Ace indices of soil bacterial communities and reshaped the bacterial community structure. Random forest model analysis revealed that soil pH and TP significantly affected soil bacterial diversity, whereas soil pH, SOM, TP, and TN significantly affected soil bacterial community structure. Correlation and structural equation modeling analyses revealed that soil TP and SOM indirectly affected potato productivity by changing soil bacterial diversity and community composition. The results of this study provide a scientific basis for improving the quality and productivity of farmland soil to guide the rational fertilization of farmlands in the agropastoral zone of northern China. Full article

Unmanned aerial vehicles (UAVs), relying on wireless communication, are inevitably influenced by the complex electromagnetic environment, attributed to the development of wireless communication technology. The modulation information of signals can assist in identifying device information and interference in the environment, which is significant for UAV communication environment monitoring. Therefore, in scenarios involving the communication of UAVs, it is necessary to find out how to perform the spectrum monitoring method to obtain the modulation information. Most existing methods are unsuitable for scenarios where multiple signals appear in the same spectrum sequence or do not use an end-to-end structure. Firstly, we established a spectrum dataset to simulate the UAV communication environment and developed a label method. Then, detection networks were employed to extract the presence and location information of signals in the spectrum. Finally, decision-level fusion was used to combine the output results of multiple nodes. Five modulation types, including ASK, FSK, 16QAM, DSB-SC, and SSB, were used to simulate different signal sources in the communication environment. Accuracy, recall, and F1 score were used as evaluation metrics. The networks were tested at different signal-to-noise ratios (SNRs). Among the different modulation types, FSK exhibits the most stable recognition performance across different models. The proposed method is of great significance for wireless radio spectrum monitoring in complex electromagnetic environments and is adaptable to scenarios where multiple receivers are used in vast terrains, providing a deep learning-based approach to radio monitoring solutions for UAV communication. Full article

The maximum quantum efficiency of photosystem II (Fv/Fm) is a widely used indicator of photosynthetic health in plants. Remote sensing of Fv/Fm using MS (multispectral) and RGB imagery has the potential to enable high-throughput screening of plant health in agricultural and ecological applications. This study aimed to estimate Fv/Fm in spring wheat at an experimental base in Hanghou County, Inner Mongolia, from 2020 to 2021. RGB and MS images were obtained at the wheat flowering stage using a Da-Jiang Phantom 4 multispectral drone. A total of 51 vegetation indices were constructed, and the measured Fv/Fm of wheat on the ground was obtained simultaneously using a Handy PEA plant efficiency analyzer. The performance of 26 machine learning algorithms for estimating Fv/Fm using RGB and multispectral imagery was compared. The findings revealed that a majority of the multispectral vegetation indices and approximately half of the RGB vegetation indices demonstrated a strong correlation with Fv/Fm, as evidenced by an absolute correlation coefficient greater than 0.75. The Gradient Boosting Regressor (GBR) was the optimal estimation model for RGB, with the important features being RGBVI and ExR. The Huber model was the optimal estimation model for MS, with the important feature being MSAVI2. The Automatic Relevance Determination (ARD) was the optimal estimation model for the combination (RGB + MS), with the important features being SIPI, ExR, and VEG. The highest accuracy was achieved using the ARD model for estimating Fv/Fm with RGB + MS vegetation indices on the test sets (Test set MAE = 0.019, MSE = 0.001, RMSE = 0.024, R² = 0.925, RMSLE = 0.014, MAPE = 0.026). The combined analysis suggests that extracting vegetation indices (SIPI, ExR, and VEG) from RGB and MS remote images by UAV as input variables of the model and using the ARD model can significantly improve the accuracy of Fv/Fm estimation at flowering stage. This approach provides new technical support for rapid and accurate monitoring of Fv/Fm in spring wheat in the Hetao Irrigation District. Full article

Relative chlorophyll content (SPAD) is an important index for characterizing the nitrogen nutrient status of plants. Continuous, rapid, nondestructive, and accurate estimation of SPAD values in wheat after heading stage can positively impact subsequent nitrogen fertilization management strategies, which regulate grain filling and yield quality formation. In this study, the estimation of SPAD of leaf relative chlorophyll content in spring wheat was conducted at the experimental base in Wuyuan County, Inner Mongolia in 2021. Multispectral images of different nitrogen application levels at 7, 14, 21, and 28 days after the wheat heading stage were acquired by DJI P4M UAV. A total of 26 multispectral vegetation indices were constructed, and the measured SPAD values of wheat on the ground were obtained simultaneously using a handheld chlorophyll meter. Four machine learning algorithms, including deep neural networks (DNN), partial least squares (PLS), random forest (RF), and Adaptive Boosting (Ada) were used to construct SPAD value estimation models at different time from heading growth stages. The model’s progress was evaluated by the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAPE). The results showed that the optimal SPAD value estimation models for different periods of independent reproductive growth stages of wheat were different, with PLS as the optimal estimation model at 7 and 14 days after heading, RF as the optimal estimation model at 21 days after heading, and Ada as the optimal estimation model at 28 d after heading. The highest accuracy was achieved using the PLS model for estimating SPAD values at 14 d after heading (training set R² = 0.767, RMSE = 3.205, MAPE = 0.060, and R² = 0.878, RMSE = 2.405, MAPE = 0.045 for the test set). The combined analysis concluded that selecting multiple vegetation indices as input variables of the model at 14 d after heading stage and using the PLS model can significantly improve the accuracy of SPAD value estimation, provides a new technical support for rapid and accurate monitoring of SPAD values in spring wheat. Full article