Lightweight Structure-aware Transformer Network for VHR Remote Sensing Image Change Detection

Popular Transformer networks have been successfully applied to remote sensing (RS) image change detection (CD) identifications and achieve better results than most convolutional neural networks (CNNs), but they still suffer from two main problems. First, the computational complexity of the Transformer grows quadratically with the increase of image spatial resolution, which is unfavorable to very high-resolution (VHR) RS images. Second, these popular Transformer networks tend to ignore the importance of fine-grained features, which results in poor edge integrity and internal tightness for largely changed objects and leads to the loss of small changed objects. To address the above issues, this Letter proposes a Lightweight Structure-aware Transformer (LSAT) network for RS image CD. The proposed LSAT has two advantages. First, a Cross-dimension Interactive Self-attention (CISA) module with linear complexity is designed to replace the vanilla self-attention in visual Transformer, which effectively reduces the computational complexity while improving the feature representation ability of the proposed LSAT. Second, a Structure-aware Enhancement Module (SAEM) is designed to enhance difference features and edge detail information, which can achieve double enhancement by difference refinement and detail aggregation so as to obtain fine-grained features of bi-temporal RS images. Experimental results show that the proposed LSAT achieves significant improvement in detection accuracy and offers a better tradeoff between accuracy and computational costs than most state-of-the-art CD methods for VHR RS images

Obesity-Related Metabolome and Gut Microbiota Profiles of Juvenile Göttingen Minipigs—Long-Term Intake of Fructose and Resistant Starch

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2-Diphenylphosphinoyl-acetyl as a remote directing group for the highly stereoselective synthesis of β-glycosides

The configuration of the anomeric glycosidic linkages is crucial for maintaining the biological functions and activities of carbohydrate molecules. However, their stereochemistry control in glycosylation represents one of the most challenging tasks in carbohydrate chemistry. In this report, the easily accessible 2-diphenylphosphinoyl-acetyl (DPPA) group was developed as a highly stereodirecting group for catalytic glycosylation via hydrogen-bond mediated delivery of the alcoholic acceptors. TMSOTf-catalyzed glycosylation with 6-O-DPPA glycosyl imidate donors displayed excellent β-selectivity and broad substrate scope, particularly applicable to synthesize the challenging β-configured 2-deoxy- and 2-azido-2-deoxy-glycosides from electron-deficient or bulky acceptors. Chemoselective removal of the DPPA group could be readily achieved under the mild catalysis of Ni(OTf)2, and further application was demonstrated in the synthesis of biologically important oligosaccharides, uronic acids, and 2,6-dideoxy-glycosides

Effects of Long-Term Low-Protein Diets Supplemented with Sodium Dichloroacetate and Glucose on Metabolic Biomarkers and Intestinal Microbiota of Finishing Pigs

The objective of this study was to evaluate the effects of low-protein (LP) diets supplemented with sodium dichloroacetate (DCA) and glucose (GLUC) on metabolic markers and intestinal microbiota of finishing pigs. A total of 80 crossbred growing barrows were allocated randomly to one of the five treatments, including the normal protein level diet (CON), the LP diets, LP with 120 mg/kg DCA (LP + DCA) or 1.8% glucose (LP + GLUC), and LP with 120 mg/kg DCA and 1.8% glucose (LP + DCA + GLUC). The LP diet increased the plasma HDL, triglyceride, and cholesterol concentrations and reduced the bile acid, urea nitrogen, albumin, and total protein concentrations compared to the CON diet (p p p < 0.05). Moreover, the LP diets with or without DCA and GLUC supplementation increased the relative abundance of colonic microbiota related to carbohydrate fermentation in finishing pigs. In conclusion, 120 mg/kg DCA or 1.8% GLUC supplementation in an LP diet modulated the hepatic lipid metabolism of pigs, while the DCA along with GLUC supplementation likely improved the lipid metabolism by stimulating bile acid secretion

Obesity-Related Metabolome and Gut Microbiota Profiles of Juvenile Göttingen Minipigs—Long-Term Intake of Fructose and Resistant Starch

publishedVersio

Obesity-Related Metabolome and Gut Microbiota Profiles of Juvenile Göttingen Minipigs—Long-Term Intake of Fructose and Resistant Starch

The metabolome and gut microbiota were investigated in a juvenile Göttingen minipig model. This study aimed to explore the metabolic effects of two carbohydrate sources with different degrees of risk in obesity development when associated with a high fat intake. A high-risk (HR) high-fat diet containing 20% fructose was compared to a control lower-risk (LR) high-fat diet where a similar amount of carbohydrate was provided as a mix of digestible and resistant starch from high amylose maize. Both diets were fed ad libitum. Non-targeted metabolomics was used to explore plasma, urine, and feces samples over five months. Plasma and fecal short-chain fatty acids were targeted and quantified. Fecal microbiota was analyzed using genomic sequencing. Data analysis was performed using sparse multi-block partial least squares regression. The LR diet increased concentrations of fecal and plasma total short-chain fatty acids, primarily acetate, and there was a higher relative abundance of microbiota associated with acetate production such as Bacteroidetes and Ruminococcus. A higher proportion of Firmicutes was measured with the HR diet, together with a lower alpha diversity compared to the LR diet. Irrespective of diet, the ad libitum exposure to the high-energy diets was accompanied by well-known biomarkers associated with obesity and diabetes, particularly branched-chain amino acids, keto acids, and other catabolism metabolites