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- Diab, Asim, et al.
(författare)
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Neutralization of macrophage inflammatory protein 2 (MIP-2) and MIP-1α attenuates neutrophil recruitment in the central nervous system during experimental bacterial meningitis
- 1999
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Ingår i: Infection and Immunity. - 0019-9567 .- 1098-5522. ; 67:5, s. 2590-2601
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Tidskriftsartikel (refereegranskat)abstract
- Chemokines are low-molecular-weight chemotactic cytokines that have been shown to play a central role in the perivascular transmigration and accumulation of specific subsets of leukocytes at sites of tissue damage. Using in situ hybridization (ISH), we investigated the mRNA induction of macrophage inflammatory protein 2 (MIP-2), MIP-1α, monocyte chemoattractant protein 1 (MCP-1), and RANTES. Challenge of infant rats’ brains with Haemophilus influenzae type b intraperitoneally resulted in the time-dependent expression of MIP-2, MIP-1α, MCP-1, and RANTES, which was maximal 24 to 48 h postinoculation. Immunohistochemistry showed significant increases in neutrophils and macrophages infiltrating the meninges, the ventricular system, and the periventricular area. The kinetics of MIP-2, MIP-1α, MCP-1, and RANTES mRNA expression paralleled those of the recruitment of inflammatory cells and disease severity. Administration of anti-MIP-2 or anti-MIP-1α antibodies (Abs) resulted in significant reduction of neutrophils. Administration of anti-MCP-1 Abs significantly decreased macrophage infiltration. Combined studies of ISH and immunohistochemistry showed that MIP-2- and MIP-1α-positive cells were neutrophils and macrophages. MCP-1-positive cells were neutrophils, macrophages, and astrocytes. Expression of RANTES was localized predominantly to resident astrocytes and microglia. The present study indicates that blocking of MIP-2 or MIP-1α bioactivity in vivo results in decreased neutrophil influx. These data are also the first demonstration that the C-C chemokine MIP-1α is involved in neutrophil recruitment in vivo.
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| 2. |
- Gao, Xiang, et al.
(författare)
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Planting Age Identification and Yield Prediction of Apple Orchard Using Time-Series Spectral Endmember and Logistic Growth Model
- 2023
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 15:3, s. 642-642
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Tidskriftsartikel (refereegranskat)abstract
- In response to significant shifts in dietary and lifestyle preferences, the global demand for fruits has increased dramatically, especially for apples, which are consumed worldwide. Growing apple orchards of more productive and higher quality with limited land resources is the way forward. Precise planting age identification and yield prediction are indispensable for the apple market in terms of sustainable supply, price regulation, and planting management. The planting age of apple trees significantly determines productivity, quality, and yield. Therefore, we integrated the time-series spectral endmember and logistic growth model (LGM) to accurately identify the planting age of apple orchard, and we conducted planting age-driven yield prediction using a neural network model. Firstly, we fitted the time-series spectral endmember of green photosynthetic vegetation (GV) with the LGM. By using the four-points method, the environmental carrying capacity (ECC) in the LGM was available, which serves as a crucial parameter to determine the planting age. Secondly, we combined annual planting age with historical apple yield to train the back propagation (BP) neural network model and obtained the predicted apple yields for 12 counties. The results show that the LGM method can accurately estimate the orchard planting age, with Mean Absolute Error (MAE) being 1.76 and the Root Mean Square Error (RMSE) being 2.24. The strong correlation between orchard planting age and apple yield was proved. The results of planting age-driven yield prediction have high accuracy, with the MAE up to 2.95% and the RMSE up to 3.71%. This study provides a novel method to accurately estimate apple orchard planting age and yields, which can support policy formulation and orchard planning in the future.
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