Reconstructing the trade history: provenance study of Han bronze mirrors in and out of Han China

A rapidly increasing number of bronze mirrors dated to the Chinese Han dynasty (202 BC – AD 220), known for their unique decorative patterns and highly developed alloying techniques, have been widely discovered in both China and beyond, providing fresh materials and scientific data to revisit their geological provenance, production and circulation network along the ancient Silk Road. In this paper, 47 bronze mirrors unearthed in the southeastern provinces of China, including Zhejiang, Anhui and Fujian provinces, have been characterized by typo-chronology, lead isotopic analysis, compositional analysis and metallography. A much wider comparative study is also carried out through a combination of data from China, Japan, Central Asia, and Southeast Asia, leading to a more updated lead isotopic database of the Han mirrors spreading out of China in various directions. Compared with the traditional ‘optimal’ model based on the Han mirrors recovered in Japan, the current study contributes several key changes in the bronze mirror production of the Han dynasty. The systematic analysis of the alloy composition, trace elements and typological studies shows that the bronze mirror industry shifted towards a more standardized production in the middle to late Western Han Dynasty. In contrast to the substantial change of non-mirror bronze productions, the similar distribution of lead isotope data in early and middle to late Western Han mirrors suggests that the ‘official monopoly of salt and iron’ policy was less effective for the management of lead involved in mirror production. Bronze mirrors dated to middle to late Western Han discovered outside Han-China, such as Japan, Thailand, Afghanistan, Xiongnu and the ancient Dian Kingdom, appear to be subjected to a more specific type of lead as a result of the state-centralized policy of the Western Han court

A Multi-Center Study to Evaluate the Performance of Phage Amplified Biologically Assay for Detecting TB in Sputum in the Pulmonary TB Patients

Objective: To evaluate the performance of phage amplified biologically assay (PhaB) for detecting tuberculosis (TB) in sputum in the pulmonary tuberculosis (PTB) patients. Methods: Shanghai Tuberculosis Key Laboratory of Shanghai Pulmonary Hospital participated in the project in collaboration with the laboratories of six hospitals and a total of 1660 eligible participants (1351 PTB patients and 309 non-TB patients) were included in the study. The sputum samples from the participants were detected by smear microscopy, PhaB, and Löwenstein-Jensen (L-J) culture method, respectively. Results: The overall sensitivity of PhaB were higher than that of L-J culture and smear microscopy (p,0.05). The sensitivity of PhaB for detecting smear-negative specimens was obviously higher than that of L-J culture (p,0.05). Compared with L-J culture, the overall sensitivity, specificity, PPV, NPV, ACC and Kappa value of PhaB were 98.4 (95 % Cl: 96.9–99.3), 71.6 (95% Cl: 68.4–74.6), 67.7, 98.7, 81.7 % and 0.643, respectively. The detection median time of PhaB only needed 48 hours, which was significantly less than that (31 days) of L-J culture method. Conclusion: PhaB method is a rapid and sensitive method for detecting TB in sputum in PTB patients; especially for th

Evaluation of a Novel Biphasic Culture Medium for Recovery of Mycobacteria: A Multi-Center Study

on L-J slants. Automated liquid culture systems are expensive. A low-cost culturing medium capable of rapidly indicating the presence of mycobacteria is needed. The aim of this study was to develop and evaluate a novel biphasic culture medium for the recovery of mycobacteria from clinical sputum specimens from suspected pulmonary tuberculosis patients.<0.001).

Characterizing Spatial Patterns of Amazon Rainforest Wildfires and Driving Factors by Using Remote Sensing and GIS Geospatial Technologies

Known as the “lung of the planet”, the Amazon rainforest produces more than 20% of the Earth’s oxygen. Once a carbon pool for mitigating climate change, the Brazilian Amazônia Biome recently has become a significant carbon emitter due to increasingly frequent wildfires. Therefore, it is of crucial importance for authorities to understand wildfire dynamics to manage them safely and effectively. This study incorporated remote sensing and spatial statistics to study both the spatial distribution of wildfires during 2019 and their relationships to 15 environmental and anthropogenic factors. First, broad-scale spatial patterns of wildfire occurrence were explored using kernel density estimation, Moran’s I, Getis-Ord Gi*, and optimized hot spot analysis (OHSA). Second, the relationships between wildfire occurrence and the environmental and anthropogenic factors were explored using several regression models, including Ordinary Least Squares (OLS), global (quasi) Poisson, Geographically-weighted Gaussian Regression (GWGR), and Geographically-weighted Poisson Regression (GWPR). The spatial analysis results indicate that wildfires exhibited pronounced regional differences in spatial patterns in the vast and heterogeneous territory of the Amazônia Biome. The GWPR model outperformed the other regression models and explained the distribution and frequency of wildfires in the Amazônia Biome as a function of topographic, meteorologic, and environmental variables. Environmental factors like elevation, slope, relative humidity, and temperature were significant factors in explaining fire frequency in localized hotspots, while factors related to deforestation (forest loss, forest fragmentation measures, agriculture) explained wildfire activity over much of the region. Therefore, this study could improve a comprehensive study on, and understanding of, wildfire patterns and spatial variation in the target areas to support agencies as they prepare and plan for wildfire and land management activities in the Amazônia Biome

Characterizing Spatial Patterns of Amazon Rainforest Wildfires and Driving Factors by Using Remote Sensing and GIS Geospatial Technologies

Known as the &ldquo;lung of the planet&rdquo;, the Amazon rainforest produces more than 20% of the Earth&rsquo;s oxygen. Once a carbon pool for mitigating climate change, the Brazilian Amaz&ocirc;nia Biome recently has become a significant carbon emitter due to increasingly frequent wildfires. Therefore, it is of crucial importance for authorities to understand wildfire dynamics to manage them safely and effectively. This study incorporated remote sensing and spatial statistics to study both the spatial distribution of wildfires during 2019 and their relationships to 15 environmental and anthropogenic factors. First, broad-scale spatial patterns of wildfire occurrence were explored using kernel density estimation, Moran&rsquo;s I, Getis-Ord Gi*, and optimized hot spot analysis (OHSA). Second, the relationships between wildfire occurrence and the environmental and anthropogenic factors were explored using several regression models, including Ordinary Least Squares (OLS), global (quasi) Poisson, Geographically-weighted Gaussian Regression (GWGR), and Geographically-weighted Poisson Regression (GWPR). The spatial analysis results indicate that wildfires exhibited pronounced regional differences in spatial patterns in the vast and heterogeneous territory of the Amaz&ocirc;nia Biome. The GWPR model outperformed the other regression models and explained the distribution and frequency of wildfires in the Amaz&ocirc;nia Biome as a function of topographic, meteorologic, and environmental variables. Environmental factors like elevation, slope, relative humidity, and temperature were significant factors in explaining fire frequency in localized hotspots, while factors related to deforestation (forest loss, forest fragmentation measures, agriculture) explained wildfire activity over much of the region. Therefore, this study could improve a comprehensive study on, and understanding of, wildfire patterns and spatial variation in the target areas to support agencies as they prepare and plan for wildfire and land management activities in the Amaz&ocirc;nia Biome

Use of Satellite Remote Sensing Data for Modeling Carbon Emissions from Fires: A Perspective in North America

Accurate accounting of carbon cycling is paramount to understanding and modeling global climate change. At present, a considerable amount of global carbon uptake (∼2 Gt/year) remains unaccounted for in the carbon budget. It has been argued that the missing carbon may be absorbed in the terrestrial biomes of the Northern Hemisphere (Tans et al., 1990), in particular the temperate and boreal forests in North America (NA), which could account for the bulk (1.7 Gt/year) of the missing carbon (Fan et al., 1998). Fire is a driving factor controlling the carbon dynamics in NA, which affects both the sign and magnitude of the carbon budget (Stocks et al., 1996; Conard and Ivanova, 1997; Kasischke, 2000). According to the modeling results of Chen et al. (2000), boreal forests in NA have undergone tremendous fluctuations in its carbon budget over the last 200 years (Fig. 18.1). Around 1880 when widespread severe fires released a huge amount of carbon into the atmosphere, the forests were a very large source of carbon (∼140 GtC/year) while around 1940, the forests became a very large sink of carbon (∼200 GtC/year) due to fast forest regeneration that absorbed a large quantity of atmospheric carbon. The net carbon exchange is now so small that it is being debated whether the boreal forest is currently a sink (Chen et al., 2000) or a source (Kurz et al., 1995). The close correlation between the carbon budget and fire activity demonstrates the importance of the accurate estimation of carbon emissions from fires