Time tracking of different cropping patterns using Landsat images under different agricultural systems during 1990-2050 in Cold China

Rapid cropland reclamation is underway in Cold China in response to increases in food demand, while the lack analyses of time series cropping pattern mappings limits our understanding of the acute transformation process of cropland structure and associated environmental effects. The Cold China contains different agricultural systems (state and private farming), and such systems could lead to different cropping patterns. So far, such changes have not been revealed yet. Based on the Landsat images, this study tracked cropping information in five-year increments (1990-1995, 1995-2000, 2000-2005, 2005-2010, and 2010-2015) and predicted future patterns for the period of 2020-2050 under different agricultural systems using developed method for determining cropland patterns. The following results were obtained: The available time series of Landsat images in Cold China met the requirements for long-term cropping pattern studies, and the developed method exhibited high accuracy (over 91%) and obtained precise spatial information. A new satellite evidence was observed that cropping patterns significantly differed between the two farm types, with paddy field in state farming expanding at a faster rate (from 2.66 to 68.56%) than those in private farming (from 10.12 to 34.98%). More than 70% of paddy expansion was attributed to the transformation of upland crop in each period at the pixel level, which led to a greater loss of upland crop in state farming than private farming (9505.66 km(2) vs. 2840.29 km(2)) during 1990-2015. Rapid cropland reclamation is projected to stagnate in 2020, while paddy expansion will continue until 2040 primarily in private farming in Cold China. This study provides new evidence for different land use change pattern mechanisms between different agricultural systems, and the results have significant implications for understanding and guiding agricultural system development

City-level comparison of urban land-cover configurations from 2000-2015 across 65 countries within the global Belt and Road

The configuration of urban land-covers is essential for improving dwellers' environments and ecosystem services. A city-level comparison of land-cover changes along the Belt and Road is still unavailable due to the lack of intra-urban land products. A synergistic classification methodology of sub-pixel un-mixing, multiple indices, decision tree classifier, unsupervised (SMDU) classification was established in the study to examine urban land covers across 65 capital cities along the Belt and Road during 2000-2015. The overall accuracies of the 15 m resolution urban products (i.e., the impervious surface area, vegetation, bare soil, and water bodies) derived from Landsat Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) images were 92.88% and 93.19%, with kappa coefficients of 0.84 and 0.85 in 2000 and 2015, respectively. The built-up areas of 65 capital cities increased from 23,696.25 km(2) to 29,257.51 km(2), with an average growth rate of 370.75 km(2)/y during 2000-2015. Moreover, urban impervious surface area (ISA) expanded with an average rate of 401.92 km(2)/y, while the total area of urban green space (UGS) decreased with an average rate of 17.59 km(2)/y. In different regions, UGS changes declined by 7.37% in humid cities but increased by 14.61% in arid cities. According to the landscape ecology indicators, urban land-cover configurations became more integrated (oShannon's Diversity Index (SHDI) = -0.063; oPatch Density (PD) = 0.054) and presented better connectivity (oConnectance Index (CON) = +0.594). The proposed method in this study improved the separation between ISA and bare soil in mixed pixels, and the 15 m intra-urban land-cover product provided essential details of complex urban landscapes and urban ecological needs compared with contemporary global products. These findings provide valuable information for urban planners dealing with human comfort and ecosystem service needs in urban areas

Berberine Moderates Glucose and Lipid Metabolism through Multipathway Mechanism

Berberine is known to improve glucose and lipid metabolism disorders, but the mechanism is still under investigation. In this paper, we explored the effects of berberine on the weight, glucose levels, lipid metabolism, and serum insulin of KKAy mice and investigated its possible glucose and lipid-regulating mechanism. We randomly divided KKAy mice into two groups: berberine group (treated with 250 mg/kg/d berberine) and control group. Fasting blood glucose (FBG), weight, total cholesterol (TC), triglyceride (TG), high-density lipoprotein-cholesterol (HDL-c), low-density lipoprotein-cholesterol (LDL-c), and fasting serum insulin were measured in both groups. The oral glucose tolerance test (OGTT) was performed. RT2 PCR array gene expression analysis was performed using skeletal muscle of KKAy mice. Our data demonstrated that berberine significantly decreased FBG, area under the curve (AUC), fasting serum insulin (FINS), homeostasis model assessment insulin resistance (HOMA-IR) index, TC, and TG, compared with those of control group. RT2 profiler PCR array analysis showed that berberine upregulated the expression of glucose transporter 4 (GLUT4), mitogen-activated protein kinase 14 (MAPK14), MAPK8(c-jun N-terminal kinase, JNK), peroxisome proliferator-activated receptor α (PPARα), uncoupling protein 2 (UCP2), and hepatic nuclear factor 4α(HNF4α), whereas it downregulated the expression of PPARγ, CCAAT/enhancer-binding protein (CEBP), PPARγ coactivator 1α(PGC 1α), and resistin. These results suggest that berberine moderates glucose and lipid metabolism through a multipathway mechanism that includes AMP-activated protein kinase-(AMPK-) p38 MAPK-GLUT4, JNK pathway, and PPARα pathway

B serum proteome profiles revealed dysregulated proteins and mechanisms associated with insomnia patients: A preliminary study

BackgroundInsomnia is a clinical problem of significant public health importance; however, the underlying pathogenesis of this disorder is not comprehensively understood.MethodsTo identify potential treatment targets and unfold one of the gaps that were involved in insomnia pathological mechanisms, we employed a tandem mass tag-based (TMT) quantitative proteomics technology to detect differentially expressed proteins (DEPs) in serum from patients with insomnia and controls. DEPs were further analyzed by bioinformatics platforms. In addition, parallel reaction monitoring (PRM) was used to verify the TMT results.ResultsPatients with insomnia had poorer sleep quality compared with healthy controls. A total of 106 DEPs were identified among patients with insomnia and controls. They were mainly enriched in immune and inflammation-related biological functions and signaling pathways. Using the protein–protein interaction network, we screened the 10 most connected proteins as key DEPs. We predicted that four key DEPs were subject to targeted regulation by natural compounds of herbs. Eight key DEPs were validated using PRM in an additional 15 patients with insomnia and 15 controls, and the results also supported the experimental findings.ConclusionWe identified aberrantly expressed proteins in insomnia that may be involved in the immune-inflammatory response. The 10 key DEPs screened may be potential targets for insomnia, especially FN1, EGF, HP, and IGF1. The results of this study will broaden our understanding of the pathological mechanisms of insomnia and provide more possibilities for pharmacotherapy