Housing over time and over the life cycle: a structural estimation

We estimate a structural model of optimal life-cycle housing and consumption in the presence of realistic labor income and house price uncertainties. The model postulates constant elasticity of substitution between housing service and nonhousing consumption, and explicitly incorporates a house adjustment cost. Our estimation fits the cross-sectional and time-series household wealth and housing profiles from the Panel Study of Income Dynamics quite well, and suggests an intra-temporal elasticity of substitution between housing and nonhousing consumption of 0.33 and a housing adjustment cost that amounts to about 15 percent of house value. Policy experiments with estimated preference parameters imply that households respond nonlinearly to house price changes with large house price declines leading to sizable decreases in both the aggregate homeownership rate and aggregate non-housing consumption. The average marginal propensity to consume out of housing wealth changes ranges from 0.4 percent to 6 percent. When lending conditions are tightened in the form of a higher down payment requirement, interestingly, large house price declines result in more severe drops in the aggregate homeownership rate but milder decreases in nonhousing consumption.

Separation of anthracene and carbazole from crude anthracene via imidazolium-based ionic liquids

Anthracene and carbazole, the main components of crude anthracene, have high added value in chemical in-dustry. In this study, two imidazolium-based ILs, [PM2IM][TFAc] (ILa) and [PM2IM][Ac] (ILb), were synthesized to separate anthracene and carbazole. The effective separation of anthracene and carbazole was achieved by hydrogen bonding between carbazole and imidazolium-based ILs. The results of density functional theory simulation and 1H NMR verification showed that the hydrogen bond interaction between carbazole and ILs was via C=O...H-N, and the hydrogen bond strength between ILb and carbazole was stronger than that of ILa. Based on the process for the separation of anthracene and carbazole from crude anthracene by imidazolium-based ILs, the purity of refining anthracene reached 94.89% and that of crude carbazole reached 81.60%. In addition, this process test realized recovery and recycling of ILs

Separation of anthracene and carbazole from crude anthracene via imidazolium-based ionic liquids

Anthracene and carbazole, the main components of crude anthracene, have high added value in chemical in-dustry. In this study, two imidazolium-based ILs, [PM2IM][TFAc] (ILa) and [PM2IM][Ac] (ILb), were synthesized to separate anthracene and carbazole. The effective separation of anthracene and carbazole was achieved by hydrogen bonding between carbazole and imidazolium-based ILs. The results of density functional theory simulation and 1H NMR verification showed that the hydrogen bond interaction between carbazole and ILs was via C=O...H-N, and the hydrogen bond strength between ILb and carbazole was stronger than that of ILa. Based on the process for the separation of anthracene and carbazole from crude anthracene by imidazolium-based ILs, the purity of refining anthracene reached 94.89% and that of crude carbazole reached 81.60%. In addition, this process test realized recovery and recycling of ILs

Phenotyping of Arabidopsis Drought Stress Response Using Kinetic Chlorophyll Fluorescence and Multicolor Fluorescence Imaging

Plant responses to drought stress are complex due to various mechanisms of drought avoidance and tolerance to maintain growth. Traditional plant phenotyping methods are labor-intensive, time-consuming, and subjective. Plant phenotyping by integrating kinetic chlorophyll fluorescence with multicolor fluorescence imaging can acquire plant morphological, physiological, and pathological traits related to photosynthesis as well as its secondary metabolites, which will provide a new means to promote the progress of breeding for drought tolerant accessions and gain economic benefit for global agriculture production. Combination of kinetic chlorophyll fluorescence and multicolor fluorescence imaging proved to be efficient for the early detection of drought stress responses in the Arabidopsis ecotype Col-0 and one of its most affected mutants called reduced hyperosmolality-induced [Ca2+]i increase 1. Kinetic chlorophyll fluorescence curves were useful for understanding the drought tolerance mechanism of Arabidopsis. Conventional fluorescence parameters provided qualitative information related to drought stress responses in different genotypes, and the corresponding images showed spatial heterogeneities of drought stress responses within the leaf and the canopy levels. Fluorescence parameters selected by sequential forward selection presented high correlations with physiological traits but not morphological traits. The optimal fluorescence traits combined with the support vector machine resulted in good classification accuracies of 93.3 and 99.1% for classifying the control plants from the drought-stressed ones with 3 and 7 days treatments, respectively. The results demonstrated that the combination of kinetic chlorophyll fluorescence and multicolor fluorescence imaging with the machine learning technique was capable of providing comprehensive information of drought stress effects on the photosynthesis and the secondary metabolisms. It is a promising phenotyping technique that allows early detection of plant drought stress

Chlorophyll Fluorescence Imaging Uncovers Photosynthetic Fingerprint of Citrus Huanglongbing

Huanglongbing (HLB) is one of the most destructive diseases of citrus, which has posed a serious threat to the global citrus production. This research was aimed to explore the use of chlorophyll fluorescence imaging combined with feature selection to characterize and detect the HLB disease. Chlorophyll fluorescence images of citrus leaf samples were measured by an in-house chlorophyll fluorescence imaging system. The commonly used chlorophyll fluorescence parameters provided the first screening of HLB disease. To further explore the photosynthetic fingerprint of HLB infected leaves, three feature selection methods combined with the supervised classifiers were employed to identify the unique fluorescence signature of HLB and perform the three-class classification (i.e., healthy, HLB infected, and nutrient deficient leaves). Unlike the commonly used fluorescence parameters, this novel data-driven approach by using the combination of the mean fluorescence parameters and image features gave the best classification performance with the accuracy of 97%, and presented a better interpretation for the spatial heterogeneity of photochemical and non-photochemical components in HLB infected citrus leaves. These results imply the potential of the proposed approach for the citrus HLB disease diagnosis, and also provide a valuable insight for the photosynthetic response to the HLB disease

Application of UAV-Based Imaging and Deep Learning in Assessment of Rice Blast Resistance

Rice blast is regarded as one of the major diseases of rice. Screening rice genotypes with high resistance to rice blast is a key strategy for ensuring global food security. Unmanned aerial vehicles (UAV)-based imaging, coupled with deep learning, can acquire high-throughput imagery related to rice blast infection. In this study, we developed a segmented detection model (called RiceblastSegMask) for rice blast detection and resistance evaluation. The feasibility of different backbones and target detection models was further investigated. RiceblastSegMask is a two-stage instance segmentation model, comprising an image-denoising backbone network, a feature pyramid, a trinomial tree fine-grained feature extraction combination network, and an image pixel codec module. The results showed that the model combining the image-denoising and fine-grained feature extraction based on the Swin Transformer and the feature pixel matching feature labels with the trinomial tree recursive algorithm performed the best. The overall accuracy for instance segmentation of RiceblastSegMask reached 97.56%, and it demonstrated a satisfactory accuracy of 90.29% for grading unique resistance to rice blast. These results indicated that low-altitude remote sensing using UAV, in conjunction with the proposed RiceblastSegMask model, can efficiently calculate the extent of rice blast infection, offering a new phenotypic tool for evaluating rice blast resistance on a field scale in rice breeding programs

Interpenetrated nano- and submicro-fibrous biomimetic scaffolds towards enhanced mechanical and biological performances

Supplementary data to this article can be found online at https:// doi.org/10.1016/j.msec.2019.110416.Developing fibrous scaffolds with hierarchical structures that closely mimic natural extracellular matrix (ECM) is highly desirable. However, fabricating scaffolds with true nanofibers (90%. Additionally, the combination of CA submicrofibers with BC nanofibers leads to significantly improved mechanical properties over nanofibrous BC and submicrofibrous CA scaffolds and enlarged pores over nanofibrous BC scaffold. In addition, the biological behaviors of prepared BC/CA on MC3T3-E1 cells were investigated. Results suggested that BC/CA scaffold is beneficial for cell migration and proliferation. Moreover, the BC/CA scaffold shows higher alkaline phosphatase (ALP) activity, and calcium depositions. In addition, the hierarchical structures can effectively improve the expression of osteogenic gene (ALP mRNA and Runx2 mRNA) and protein (ALP). We believe that the methodology might provide biomimetic morphological microenvironments for enhanced tissue regeneration.This work is supported by the National Natural Science Foundation of China (Grant nos. 51973058, 31870963, 51572187, and 51563008), the Excellent Young Scientists Fund by National Natural Science Foundation of China (No. 31722022), the Youth Science Foundation of Jiangxi Province (No. 20171ACB21036 and 20181BAB216010).info:eu-repo/semantics/publishedVersio