Life Cycle Energy Analysis of Reclaimed Water Reuse Projects in Beijing.

To illustrate the benefits of water reuse project, the process-based life cycle analysis (LCA) could be combined with input-output LCA to evaluate the water reuse project. Energy is the only evaluation parameter used in this study. Life cycle assessment of all energy inputs (LCEA) is completed mainly by the life cycle inventory (LCI), taking into account the full life cycle including the construction, the operation, and the demolition phase of the project. Assessment of benefit from water reuse during the life cycle should focus on wastewater discharge reduction and water-saving benefits. The results of LCEA of Beijing water reuse project built in 2014 in a comprehensive way shows that the benefits obtained from the reclaimed water reuse far exceed the life cycle energy consumption. In this paper, the authors apply the LCEA model to estimate the benefits of reclaimed water reuse projects quantitatively.

De novo sequencing and comparative transcriptome analysis of adventitious root development induced by exogenous indole-3-butyric acid in cuttings of tetraploid black locust.

BACKGROUND: Indole-3-butyric acid (IBA) is applied to the cuttings of various plant species to induce formation of adventitious roots (ARs) in commercial settings. Tetraploid black locust is an attractive ornamental tree that is drought resistant, sand tolerant, can prevent sand erosion and has various commercial uses. To further elucidate the mechanisms of AR formation, we used Illumina sequencing to analyze transcriptome dynamics and differential gene expression at four developmental stages in control (CK) and IBA-treated groups. RESULTS: The short reads were assembled into 127,038 unitranscripts and 101,209 unigenes, with average lengths of 986 and 852 bp. In total, 10,181 and 14,924 differentially expressed genes (DEGs) were detected in the CK and IBA-treated groups, respectively. Comparison of the four consecutive developmental stages showed that 282 and 260 DEGs were shared between IBA-treated and CK, suggesting that IBA treatment increased the number of DEGs. We observed 1,721 up-regulated and 849 down-regulated genes in CI vs. II, 849 up-regulated and 836 down-regulated genes in CC vs. IC, 881 up-regulated and 631 down-regulated genes in CRP vs. IRP, and 5,626 up-regulated and 4,932 down-regulated genes in CAR vs. IAR, of which 25 up-regulated DEGs were common to four pairs, and these DEGs were significantly up-regulated at AR. These results suggest that substantial changes in gene expression are associated with adventitious rooting. GO functional category analysis indicated that IBA significantly up- or down-regulated processes associated with regulation of transcription, transcription of DNA dependent, integral to membrane and ATP binding during the development process. KEGG pathway enrichment indicated that glycolysis/gluconeogenesis, cysteine and methionine metabolism, photosynthesis, nucleotide sugar metabolism, and lysosome were the pathways most highly regulated by IBA. We identified a number of differentially regulated unigenes, including 12 methionine-related genes and 12 ethylene-related genes, associated with the KEGG pathway cysteine and methionine metabolism. The GO enrichment, pathway mapping, and gene expression profile analyses revealed molecular traits for root induction and initiation. CONCLUSION: Our study presents a global view of the transcriptomic profiles of tetraploid black locust cuttings in response to IBA treatment and provides new insights into the fundamental mechanisms associated with auxin-induced adventitious rooting.

Using Spectral Reflectance to Estimate the Leaf Chlorophyll Content of Maize Inoculated With Arbuscular Mycorrhizal Fungi Under Water Stress.

Leaf chlorophyll content is an important indicator of the growth and photosynthesis of maize under water stress. The promotion of maize physiological growth by (AMF) has been studied. However, studies of the effects of AMF on the leaf chlorophyll content of maize under water stress as observed through spectral information are rare. In this study, a pot experiment was carried out to spectrally estimate the leaf chlorophyll content of maize subjected to different durations (20, 35, and 55 days); degrees of water stress (75%, 55% and 35% water supply) and two inoculation treatments (inoculation with Funneliformis mosseae and no inoculation). Three machine learning algorithms, including the back propagation (BP) method, least square support vector machine (LSSVM) and random forest (RF) method, were used to estimate the leaf chlorophyll content of maize. The results showed that AMF increased the leaf chlorophyll content, net photosynthetic rate (A), stomatal conductance (gs), transpiration rate (E), and water use efficiency (WUE) of maize but decreased the intercellular carbon dioxide concentration (Ci) of maize and atmospheric vapor pressure deficit (VPD) regardless of the water stress duration and degree. The first-order differential spectral data can better reflect the correlation between leaf chlorophyll content and spectrum of inoculated maize when compared with original spectral data. The BP model performed bestin modeling the maize leaf chlorophyll content, yielding the largest R 2-values and smallest root mean square error (RMSE) values, regardless of stress duration. These results provide a reliable basis for the effective monitoring of the leaf chlorophyll content of maize under water stress.