Identification and characterization of two critical sequences in SV40PolyA that activate the green fluorescent protein reporter gene

Alu repeats or Line-1-ORF2 (ORF2) inhibit expression of the green fluorescent protein (GFP) gene when inserted downstream of this gene in the vector pEGFP-C1. In this work, we studied cis-acting elements that eliminated the repression of GFP gene expression induced by Alu and ORF2 and sequence characteristics of these elements. We found that sense and antisense PolyA of simian virus 40 (SV40PolyA, 240 bp) eliminated the repression of GFP gene expression when inserted between the GFP gene and the Alu (283 bp) repeats or ORF2 (3825 bp) in pAlu14 (14 tandem Alu repeats were inserted downstream of the GFP gene in the vector pEGFP-C1) or pORF2. Antisense SV40PolyA (PolyAas) induced stronger gene expression than its sense orientation (PolyA). Of four 60-bp segments of PolyAas (1F1R, 2F2R, 3F3R and 4F4R) inserted independently into pAlu14, only two (2F2R and 3F3R) eliminated the inhibition of GFP gene expression induced by Alu repeats. Deletion analysis revealed that a 17 nucleotide AT repeat (17ntAT; 5′-AAAAAAATGCTTTATTT-3′) in 2F2R and the fragment 3F38d9 (5′-ATAAACAAGTTAACAACA ACAATTGCATT-3′) in 3F3R were critical sequences for activating the GFP gene. Sequence and structural analyses showed that 17ntAT and 3F38d9 included imperfect palindromes and may form a variety of unstable stem-loops. We suggest that the presence of imperfect palindromes and unstable stem-loops in DNA enhancer elements plays an important role in GFP gene activation

Anti-inflammatory role of fenofibrate in treating diseases

Inflammation contributes to the pathogenesis of several diseases. Fenofibrate, known as a peroxisome proliferator-activated receptor - α (PPAR-α) agonist, is a classic drug for treating hyperlipidemia. In addition to its lipid-lowering effect, fenofibrate has also been reported to exert anti-inflammatory effects with complicated underlying mechanisms of action. In general, the anti-inflammatory effect of fenofibrate is secondary to its lipid-lowering effect, especially for the inflammation caused by hyperlipidemia in the circulatory system. Some anti-inflammatory actions may also come from its regulatory effects on intracellular lipid metabolism by activating PPAR-α. In addition, some roles in anti-inflammation might be mediated by its direct regulation of inflammatory signaling pathways. In order to understand anti-inflammatory activities and the underlying mechanisms of fenofibrate action in disease better, we herein reviewed and discussed the anti-inflammatory roles and its subserving mechanisms in various diseases of different organ systems. Thus, this review offers insights into the optimal use of fenofibrate in the clinical setting

Spatiotemporal evolution of farmland ecosystem stability in the Fenhe River Basin China based on perturbation-resistance-response framework

Farmland is the foundation for maintaining national food security. Slow-acting, long-term press perturbations (such as soil erosion and pollution) can considerably influence fragile farmland ecosystems. In accordance with the ecosystem stability theory, a Perturbation (such as soil erosion, salinization, and desertification) -Resistance (remote sensing ecological index) -Response (net primary productivity) framework was constructed to assess farmland ecosystem stability (FES) and clarify the interactions between different subsystems after perturbations. Within this framework, an innovative and dynamic FES evaluation model was established. Moreover, the spatiotemporal variations of FES in the Fenhe River Basin were quantified by GIS and RS platforms, and its driving mechanisms were explored through path analysis. The results reveal that FES in the Fenhe River Basin increased by a total of 889 units from 2011 to 2021, covering 76.00 % of the total farmland. Perturbation mitigation was observed in 99.03 % of the farmland, with the most significant decreases in desertification and salinization. Furthermore, 69.00 % of the farmland exhibited enhanced resistance, and 87.00 % demonstrated significant improvements in productivity. Particularly, resistance was the core factor affecting FES, while soil salinization and pollution were the main perturbation indicators weakening FES. Enhanced ecological management and conservation brought about boosted farmland productivity, resistance and resilience, and ultimately FES. This study leverages spatial data to assess and monitor ecosystem stability, laying a scientific foundation for identifying ecological priority conservation areas and production restoration zones. Simultaneously, it enlightens the sustainable use and conservation of farmland

Prediction of Soil Nutrients Based on Topographic Factors and Remote Sensing Index in a Coal Mining Area, China

(1) Background: Coal mining operations caused severe land subsidence and altered the distributions of soil nutrients that influenced by multiple environmental factors at different scales. However, the prediction performances for soil nutrients based on their scale-specific relationships with influencing factors remains undefined in the coal mining area. The objective of this study was to establish prediction models of soil nutrients based on their scale-specific relationships with influencing factors in a coal mining area. (2) Methods: Soil samples were collected based on a 1 × 1 km regular grid, and contents of soil organic matter, soil available nitrogen, soil available phosphorus, and soil available potassium were measured. The scale components of soil nutrients and the influencing factors collected from remote sensing and topographic factors were decomposed by two-dimensional empirical mode decomposition (2D-EMD), and the predictions for soil nutrients were established using the methods of multiple linear stepwise regression or partial least squares regression based on original samples (MLSROri or PLSROri), partial least squares regression based on bi-dimensional intrinsic mode function (PLSRBIMF), and the combined method of 2D-EMD, PLSR, and MLSR (2D-EMDPM). (3) Results: The correlation types and correlation coefficients between soil nutrients and influencing factors were scale-dependent. The variances of soil nutrients at smaller scale were stochastic and non-significantly correlated with influencing factors, while their variances at the larger scales were stable. The prediction performances in the coal mining area were better than those in the non-coal mining area, and 2D-EMDPM had the most stable performance. (4) Conclusions: The scale-dependent predictions can be used for soil nutrients in the coal mining areas

Prediction of Soil Nutrients Based on Topographic Factors and Remote Sensing Index in a Coal Mining Area, China

(1) Background: Coal mining operations caused severe land subsidence and altered the distributions of soil nutrients that influenced by multiple environmental factors at different scales. However, the prediction performances for soil nutrients based on their scale-specific relationships with influencing factors remains undefined in the coal mining area. The objective of this study was to establish prediction models of soil nutrients based on their scale-specific relationships with influencing factors in a coal mining area. (2) Methods: Soil samples were collected based on a 1 × 1 km regular grid, and contents of soil organic matter, soil available nitrogen, soil available phosphorus, and soil available potassium were measured. The scale components of soil nutrients and the influencing factors collected from remote sensing and topographic factors were decomposed by two-dimensional empirical mode decomposition (2D-EMD), and the predictions for soil nutrients were established using the methods of multiple linear stepwise regression or partial least squares regression based on original samples (MLSROri or PLSROri), partial least squares regression based on bi-dimensional intrinsic mode function (PLSRBIMF), and the combined method of 2D-EMD, PLSR, and MLSR (2D-EMDPM). (3) Results: The correlation types and correlation coefficients between soil nutrients and influencing factors were scale-dependent. The variances of soil nutrients at smaller scale were stochastic and non-significantly correlated with influencing factors, while their variances at the larger scales were stable. The prediction performances in the coal mining area were better than those in the non-coal mining area, and 2D-EMDPM had the most stable performance. (4) Conclusions: The scale-dependent predictions can be used for soil nutrients in the coal mining areas.</jats:p