Housing Boom,Dutch Disease,and the Decline in China's Manufacturing Exports

Drawing on Dutch disease theory,we assess how the recent housing boom has contributed to a decline in China's manufacturing exports.Using Chinese city and enterprise panel data from 2004 to 2013,our analysis reveals that Dutch disease indeed exists and that the housing price increase has played a very important role in affecting China's manufacturing exports through two key channels:resource movement effect and spending effect.Specifically,this paper found that:(i)the housing price increase hindered labor flowing into China's manufacturing industry(resource movement efect)and caused higher inflation(spending effect);(ii)the housing boom clearly impeded China's manufacturing exports,especially after the outbreak of the global economic crisis in 2008;(ii)the impacts of the housing price increase on China's manufacturing exports were heterogenous,and were more significant for labor-intensive manufacturing businesses,businesses that were foreign owned,less R&D intensive,or located in the central and western regions.

Machine learning-based spectral and spatial analysis of hyper-and multi-spectral leaf images for Dutch elm disease detection and resistance screening

Diseases caused by invasive pathogens are an increasing threat to forest health,and early and accurate disease detection is essential for timely and precision forest management.The recent technological advancements in spectral imaging and artificial intelligence have opened up new possibilities for plant disease detection in both crops and trees.In this study,Dutch elm disease(DED;caused by Ophiostoma novo-ulmi,)and American elm(Ulmus americana)was used as example pathosystem to evaluate the accuracy of two in-house developed high-precision portable hyper-and multi-spectral leaf imagers combined with machine learning as new tools for forest disease detection.Hyper-and multi-spectral images were collected from leaves of American elm geno-types with varied disease susceptibilities after mock-inoculation and inoculation with O.novo-ulmi under green-house conditions.Both traditional machine learning and state-of-art deep learning models were built upon derived spectra and directly upon spectral image cubes.Deep learning models that incorporate both spectral and spatial features of high-resolution spectral leaf images have better performance than traditional machine learning models built upon spectral features alone in detecting DED.Edges and symptomatic spots on the leaves were highlighted in the deep learning model as important spatial features to distinguish leaves from inoculated and mock-inoculated trees.In addition,spectral and spatial feature patterns identified in the machine learning-based models were found relative to the DED susceptibility of elm genotypes.Though further studies are needed to assess applications in other pathosystems,hyper-and multi-spectral leaf imagers combined with machine learning show potential as new tools for disease phenotyping in trees.

DISTRIBUTION OF AN NcoI POLYMORPHISM IN THE LYMPHOTOXIN α GENE IN DUTCH PATIENTS WITH INFLAMATORY BOWEL DISEASES

An Ncol restriction fragment length polymorphism in the first intron of the lymphotoxin α gene was investigated in 35 patients with Crohn's disease, 40 patients with ulcerative colitis, and 30 unrelated healthy controls, all of Dutch origin. The results showed that no significant differences existed in the genotype frequencies of the Ncol polymorphism in the first intron of the LTα gene between ulcerative colitis patients or Crohn's disease patients and the healthy controls. The study indicates that the Ncol polymorphism in the LTa gene can not be used as a genetic marker for the predisposition to inflammatory bowel diseases. However, since this polymorphism may control the production of tumor necrosis factor, study of this and other related tumor necrosis factor gene polymorphisms may be used as markers to identify patient subgroups and to define patient heterogeneity. Further studies are being carried out on other polymorphisms and on the relevance of LTα and TNFα haplotypes.