Assessing surface albedo change and its induced radiation budget under rapid urbanization with Landsat and GLASS data

Radiative forcing (RF) induced by land use (mainly surface albedo) change is still not well understood in climate change science, especially the effects of changes in urban albedo due to rapid urbanization on the urban radiation budget. In this study, a modified RF derivation approach based on Landsat images was used to quantify changes in the solar radiation budget induced by variations in surface albedo in Beijing from 2001 to 2009. Field radiation records from a Beijing meteorological station were used to identify changes in RF at the local level. There has been rapid urban expansion over the last decade, with the urban land area increasing at about 3.3 % annually from 2001 to 2009. This has modified three-dimensional urban surface properties, resulting in lower albedo due to complex building configurations of urban centers and higher albedo on flat surfaces of suburban areas and cropland. There was greater solar radiation (6.93 × 108 W) in the urban center in 2009 than in 2001. However, large cropland and urban fringe areas caused less solar radiation absorption. RF increased with distance from the urban center (less than 14 km) and with greater urbanization, with the greatest value being 0.41 W/m2. The solar radiation budget in urban areas was believed to be mainly influenced by urban structural changes in the horizontal and vertical directions. Overall, the results presented herein indicate that cumulative urbanization impacts on the natural radiation budget could evolve into an important driver of local climate change

Leveraging 16S rRNA Microbiome Sequencing Data to Identify Bacterial Signatures for Irritable Bowel Syndrome

Irritable bowel syndrome (IBS) is a chronic gastrointestinal disorder characterized by abdominal pain or discomfort. Previous studies have illustrated that the gut microbiota might play a critical role in IBS, but the conclusions of these studies, based on various methods, were almost impossible to compare, and reproducible microorganism signatures were still in question. To cope with this problem, previously published 16S rRNA gene sequencing data from 439 fecal samples, including 253 IBS samples and 186 control samples, were collected and processed with a uniform bioinformatic pipeline. Although we found no significant differences in community structures between IBS and healthy controls at the amplicon sequence variants (ASV) level, machine learning (ML) approaches enabled us to discriminate IBS from healthy controls at genus level. Linear discriminant analysis effect size (LEfSe) analysis was subsequently used to seek out 97 biomarkers across all studies. Then, we quantified the standardized mean difference (SMDs) for all significant genera identified by LEfSe and ML approaches. Pooled results showed that the SMDs of nine genera had statistical significance, in which the abundance of Lachnoclostridium, Dorea, Erysipelatoclostridium, Prevotella 9, and Clostridium sensu stricto 1 in IBS were higher, while the dominant abundance genera of healthy controls were Ruminococcaceae UCG-005, Holdemanella, Coprococcus 2, and Eubacterium coprostanoligenes group. In summary, based on six published studies, this study identified nine new microbiome biomarkers of IBS, which might be a basis for understanding the key gut microbes associated with IBS, and could be used as potential targets for microbiome-based diagnostics and therapeutics