Spatiotemporal changes of ecological environment quality and climate drivers in Zoige Plateau.

Ecological environment is the essential material basis of human survival and connects regional economy with socially sustainable development. However, climate changes characterized by global climate warming have caused a series of ecological environmental problems in recent years. Few studies have discussed various climate factors affecting the ecological environment, and the spatial non-stationary effects of different climate factors on the ecological environment are still unclear. Dynamically monitoring ecological environment changes in fragile areas and identifying its climate-driving mechanism are essential for ecological protection and environmental repair. Taking Zoige Plateau as a case, this paper simulated the eco-environmental quality during 1987-2020 using remote sensing data, utilized Geodetector method to identify the contributions of various climate drivers to ecological environment quality, and then adopted the Geographically Weighted Regression model to explore the spatial non-stationary impacts of climate factors on ecological environment quality. The results showed that the ecological quality in the middle regions of the Zoige Plateau was slightly better than in the surrounding marginal areas. For the whole area of Zoige Plateau, the average ecological environment quality index was 54.92, 53.99, 56.17, 57.88, 63.44, 56.93, 59.43, and 59.76 in 1987, 1992, 1997, 2001, 2006, 2013, 2016 and 2020, respectively, which indicated that eco-environmental quality witnessed several fluctuations during the study period but showed a generally increasing trend. Among five climate factors, the temperature was the dominant climate factor affecting the ecological environment quality (q value: 0.11-0.19), sunshine duration (0.03-0.17), wind speed (0.03-0.11), and precipitation (0.03-0.08) were the main climate drivers, while the explanatory power of relative humidity to ecological environment quality was relatively small. Such various climate factors impacting the ecological environment quality demonstrated distinct spatial non-stationary and the range of driving impact varied with time. Temperature, sunshine duration, wind speed, and relative humidity promoted ecological environment quality in most regions (regression coefficients > 0), while precipitation mainly had a negative inhibitory impact (regression coefficients < 0). Meanwhile, the greater impacts of these five climate factors were concentrated in high-elevation regions of the south and west or the northern areas. The appropriate enhancement of climate warming and air humidity was beneficial to the improvement of the ecological environment, but the excessive precipitation would result in landslides and exhibit inhibition of vegetation growth. Therefore, selecting cold-tolerant herbs and shrubs, and strengthening climate monitoring and early warning systems (such as drought and excessive precipitation) are essential for ecological restoration.

DBU-Promoted Tandem Cyclization of Ynones and Diazo Compounds: Direct Synthesis of Eight-Membered Cyclic Ethers.

A novel DBU-promoted tandem cyclization reaction of ynones with diazo compounds as the N-terminal electrophiles has been developed. The reaction provides a simple and efficient method for the synthesis of fused eight-membered oxocino[2,3-c] pyrazoles from readily available acyclic starting materials in a single step. This reaction allows the formation of four new bonds and two rings in a highly regio- and diastereoselective manner, where two adjacent stereocenters are created simultaneously in an atom-economic manner.

Fast Semantic Segmentation of Remote Sensing Images Using a Network That Integrates Global and Local Information.

Efficient processing of ultra-high-resolution images is increasingly sought after with the continuous advancement of photography and sensor technology. However, the semantic segmentation of remote sensing images lacks a satisfactory solution to optimize GPU memory utilization and the feature extraction speed. To tackle this challenge, Chen et al. introduced GLNet, a network designed to strike a better balance between GPU memory usage and segmentation accuracy when processing high-resolution images. Building upon GLNet and PFNet, our proposed method, Fast-GLNet, further enhances the feature fusion and segmentation processes. It incorporates the double feature pyramid aggregation (DFPA) module and IFS module for local and global branches, respectively, resulting in superior feature maps and optimized segmentation speed. Extensive experimentation demonstrates that Fast-GLNet achieves faster semantic segmentation while maintaining segmentation quality. Additionally, it effectively optimizes GPU memory utilization. For example, compared to GLNet, Fast-GLNet's mIoU on the Deepglobe dataset increased from 71.6% to 72.1%, and GPU memory usage decreased from 1865 MB to 1639 MB. Notably, Fast-GLNet surpasses existing general-purpose methods, offering a superior trade-off between speed and accuracy in semantic segmentation.

A Multi-Step Fusion Network for Semantic Segmentation of High-Resolution Aerial Images.

The demand for semantic segmentation of ultra-high-resolution remote sensing images is becoming increasingly stronger in various fields, posing a great challenge with concern to the accuracy requirement. Most of the existing methods process ultra-high-resolution images using downsampling or cropping, but using this approach could result in a decline in the accuracy of segmenting data, as it may cause the omission of local details or global contextual information. Some scholars have proposed the two-branch structure, but the noise introduced by the global image will interfere with the result of semantic segmentation and reduce the segmentation accuracy. Therefore, we propose a model that can achieve ultra-high-precision semantic segmentation. The model consists of a local branch, a surrounding branch, and a global branch. To achieve high precision, the model is designed with a two-level fusion mechanism. The high-resolution fine structures are captured through the local and surrounding branches in the low-level fusion process, and the global contextual information is captured from downsampled inputs in the high-level fusion process. We conducted extensive experiments and analyses using the Potsdam and Vaihingen datasets of the ISPRS. The results show that our model has extremely high precision.

Understanding the influence of pullulan on the quality changes, water mobility, structural properties and thermal properties of frozen cooked noodles.

Pullulan is widely applied in the food industry due to its unique physicochemical properties, but little information is known about its effects on the quality of frozen cooked noodles (FCNs), nor the underlying mechanism. In this study, the addition of 0.3% and 0.5% pullulan resulted in better texture and cooking properties, and minor chrominance differences, and it significantly (P < 0.05) decreased the freezable water content and retarded the water migration. Pullulan inhibited the depolymerization of the glutenin macropolymer during 0-8 weeks of frozen storage. Meanwhile, pullulan caused slightly decreased α-helixes and increased ß-turns, as well as decreased degradation temperature, further suggesting that pullulan influenced the gluten network. A more compact microstructure was shown in the pullulan-fortified FCNs. This study provides a theoretical basis for the positive effects of pullulan on the quality of FCNs from the perspectives of water state and protein structure.

Network Pharmacology-Based and Experimental Identification of the Effects of Quercetin on Alzheimer's Disease.

Alzheimer's disease (AD) is one of the neurodegenerative brain disorders inducing nearly half of dementia cases, and the diagnosis and treatment of AD are the primary issues clinically. However, there is a lack of effective biomarkers and drugs for AD diagnosis and therapeutics so far. In this study, bioinformatics analysis combined with an experimental verification strategy was used to identify the biomarkers and the quercetin targets for AD diagnosis and treatment. First, differentially expressed genes in the AD brain were identified by microarray data analysis. Second, quercetin, a predominant flavonoid, was used to screen the target genes. Third, the drug-disease network was determined, and the target genes of quercetin treatment were obtained in AD-related HT-22 cell-based assay. Six genes, including MAPT, PIK3R1, CASP8, DAPK1, MAPK1, and CYCS, were validated by the system pharmacology analysis in the hippocampus samples of AD patients. The results suggested that MAPT, PIK3R1, CASP8, and DAPK1 were significantly increased, but MAPK1 and CYCS were significantly decreased in HT-22 cells after Aß1-42 treatment. Moreover, MAPK1 and CYCS were markedly increased, but MAPT, PIK3R1, CASP8, and DAPK1 were markedly decreased after quercetin treatment in these HT-22 cells. Altogether, MAPT, PIK3R1, CASP8, DAPK1, MAPK1, and CYCS are all the biomarkers for AD diagnosis and the targets of quercetin treatment, and our findings may provide valuable biomarkers for AD diagnosis and treatment.

trans-Resveratrol ameliorates anxiety-like behaviors and neuropathic pain in mouse model of post-traumatic stress disorder.

BACKGROUND: trans-Resveratrol has been extensively investigated for its anti-inflammatory, antioxidant, and anti-psychiatric properties. However, whether it could rescue posttraumatic stress disorder-like stress-induced pain abnormality is unknown. AIM: The present study examined the effects of trans-resveratrol on anxiety-like behavior and neuropathic pain induced by single-prolonged stress, which is a classical animal model for mimicking posttraumatic stress disorder. METHODS: The single-prolonged stress-induced anxiety-like behavior and pain response were detected by the novelty suppressed feeding, marble burying, locomotor activity, von Frey, and acetone-induced cold allodynia tests in mice. The serum corticosterone levels and glucocorticoid receptor, protein kinase A, phosphorylated cAMP response element binding protein, and brain-derived neurotrophic factor expression were detected by enzyme-linked immunosorbent assay and immunoblot analyses. RESULTS: trans-Resveratrol reversed single-prolonged stress-induced increased latency to feed and the number of marbles buried in the novelty suppressed feeding and marble burying tests, but did not significantly influence locomotion distance in the locomotor activity test. trans-Resveratrol also reversed single-prolonged stress-induced cold and mechanical allodynia. Moreover, single-prolonged stress induced abnormality in the limbic hypothalamus-pituitary-adrenal axis was reversed by trans-resveratrol, as evidenced by the fact that trans-resveratrol reversed the differential expression of glucocorticoid receptor in the anxiety- and pain-related regions. In addition, trans-resveratrol increased protein kinase A, phosphorylated cAMP response element binding protein, and brain-derived neurotrophic factor levels, which were decreased in mice subjected to single-prolonged stress. CONCLUSIONS: These results provide compelling evidence that trans-resveratrol protects neurons against posttraumatic stress disorder-like stress insults through regulation of limbic hypothalamus-pituitary-adrenal axis function and activation of downstream neuroprotective molecules such as protein kinase A, phosphorylated cAMP response element binding protein, and brain-derived neurotrophic factor expression.

The antidepressant- and anxiolytic-like effects of resveratrol: Involvement of phosphodiesterase-4D inhibition.

Resveratrol is a natural non-flavonoid polyphenol found in red wine, which has numerous pharmacological properties including anti-stress and antidepressant-like abilities. However, whether the antidepressant- and anxiolytic-like effects of resveratrol are related to the inhibition of phosphodiesterase 4 (PDE4) and its subtypes remains unknown. The same holds true for the subsequent cAMP-dependent pathway. The first set of studies investigated whether resveratrol exhibited neuroprotective effects against corticosterone-induced cell lesion as well as its underlying mechanism. We found that 100 µM corticosterone induced PDE2A, PDE3B, PDE4A, PDE4D, PDE10 and PDE11 expression in HT-22 cells, which results in significant cell lesion. However, treatment with resveratrol increased cell viability in a dose- and time-dependent manner. These effects seem related to the inhibition of PDE4D, as evidenced by resveratrol dose-dependently decreasing PDE4D expression. In addition, the PKA inhibitor H89 reversed resveratrol's effects on cell viability. Resveratrol prevented corticosterone-induced reduction in cAMP, pVASP(s157), pCREB, and BDNF levels, indicating that cAMP signaling is involved in resveratrol-induced neuroprotective effects. Not to mention, PDE4D knockdown by PDE4D siRNA potentiated the effect of low dose of resveratrol on cAMP, pVASP, pCREB, and BDNF expression, while PDE4D overexpression reversed the effect of high dose of resveratrol on the expression of the above proteins. Finally, the subsequent in vivo data supports the in vitro findings, suggesting that resveratrol-induced antidepressant- and anxiolytic-like effects are mediated by PDE4D. Overall, these findings support the hypothesis that PDE4D-mediated cAMP signaling plays an important role in resveratrol's protective effects on stress-induced depression- and anxiety-like behavior.