The interaction between miR160 and miR165/166 in the control of leaf development and drought tolerance in Arabidopsis

MicroRNAs (miRNAs) are a class of non-coding RNAs that play important roles in plant development and abiotic stresses. To date, studies have mainly focused on the roles of individual miRNAs, however, a few have addressed the interactions among multiple miRNAs. In this study, we investigated the interplay and regulatory circuit between miR160 and miR165/166 and its effect on leaf development and drought tolerance in Arabidopsis using Short Tandem Target Mimic (STTM). By crossing STTM160 Arabidopsis with STTM165/166, we successfully generated a double mutant of miR160 and miR165/166. The double mutant plants exhibited a series of compromised phenotypes in leaf development and drought tolerance in comparison to phenotypic alterations in the single STTM lines. RNA-seq and qRT-PCR analyses suggested that the expression levels of auxin and ABA signaling genes in the STTM-directed double mutant were compromised compared to the two single mutants. Our results also suggested that miR160-directed regulation of auxin response factors (ARFs) contribute to leaf development via auxin signaling genes, whereas miR165/166- mediated HD-ZIP IIIs regulation confers drought tolerance through ABA signaling. Our studies further indicated that ARFs and HD-ZIP IIIs may play opposite roles in the regulation of leaf development and drought tolerance that can be further applied to other crops for agronomic traits improvement

The interaction between miR160 and miR165/166 in the control of leaf development and drought tolerance in Arabidopsis

MicroRNAs (miRNAs) are a class of non-coding RNAs that play important roles in plant development and abiotic stresses. To date, studies have mainly focused on the roles of individual miRNAs, however, a few have addressed the interactions among multiple miRNAs. In this study, we investigated the interplay and regulatory circuit between miR160 and miR165/166 and its effect on leaf development and drought tolerance in Arabidopsis using Short Tandem Target Mimic (STTM). By crossing STTM160 Arabidopsis with STTM165/166, we successfully generated a double mutant of miR160 and miR165/166. The double mutant plants exhibited a series of compromised phenotypes in leaf development and drought tolerance in comparison to phenotypic alterations in the single STTM lines. RNA-seq and qRT-PCR analyses suggested that the expression levels of auxin and ABA signaling genes in the STTM-directed double mutant were compromised compared to the two single mutants. Our results also suggested that miR160-directed regulation of auxin response factors (ARFs) contribute to leaf development via auxin signaling genes, whereas miR165/166- mediated HD-ZIP IIIs regulation confers drought tolerance through ABA signaling. Our studies further indicated that ARFs and HD-ZIP IIIs may play opposite roles in the regulation of leaf development and drought tolerance that can be further applied to other crops for agronomic traits improvement

The risk assessment of relapse among newly enrolled participants in methadone maintenance treatment: A group-LASSO based Bayesian network study

BackgroundRelapse is a great barrier to improving the effectiveness of methadone maintenance treatment (MMT). Participants with different treatment durations could vary in their compliance with MMT, which may lead to different levels of relapse risk. This study aims to identify the risk factors for relapse and assess the relapse risk of MMT participants of different treatment durations.MethodThis retrospective study used data collected from seven MMT clinics in Guangdong Province, China, from January 2010 to April 2017. Newly enrolled participants who received 6 (n = 903) and 12 (n = 710) months of consecutive treatment with complete data were included. We selected significant risk factors for relapse through the group lasso regression and then incorporated them into Bayesian networks to reveal relationships between factors and predict the relapse risk.ResultsThe results showed that participants who received 6-month treatment had a lower relapse rate (32.0%) than those of 12-month treatment (39.0%, P < 0.05). Factors including personal living status and daily methadone dose were only influential to those who received the 6-month treatment. However, age, age at the initial drug use, HIV infection status, sexual behaviors, and continuous treatment days were common factors of both durations. The highest relapse risk for those after the 6-month treatment was inferred as 66.7% while that of the 12-month treatment was 83.3%. Farmers and those who have high accessibility to MMT services may require additional attention.ConclusionIt is necessary to implement targeted interventions and education based on the treatment durations of participants to decrease the relapse rate. Meanwhile, those about HIV/sexually transmitted infection prevention and anti-narcotics should be held in the whole process

Association of Intraoperative Hypotension with Acute Kidney Injury after Noncardiac Surgery in Patients Younger than 60 Years Old

Background/Aims: Intraoperative hypotension (IOH) may be associated with surgery-related acute kidney injury (AKI). However, the duration of hypotension that triggers AKI is poorly understood. The incidence of AKI with various durations of IOH and mean arterial pressures (MAPs) was investigated. Materials: A retrospective cohort study of 4,952 patients undergoing noncardiac surgery (2011 to 2016) with MAP monitoring and a length of stay of one or more days was performed. The exclusion criteria were a preoperative estimated glomerular filtration (eGFR) ≤60 mL min–1 1.73 m2–1, a preoperative MAP less than 65 mm Hg, dialysis dependence, urologic surgery, age older than 60 years, and a surgical duration of less than 60 min. The primary exposure was IOH, and the primary outcome was AKI (50% or 0.3 mg dL–1 increase in creatinine) during the first 7 postoperative days. Multivariable logistic regression was used to model the exposure-outcome relationship. Results: AKI occurred in 186 (3.76%) noncardiac surgery patients. The adjusted odds ratio for surgery-related AKI for a MAP of less than 55 mm Hg was 14.11 (95% confidence interval: 5.02–39.69) for an exposure of more than 20 min. Age was not an interaction factor between AKI and IOH. Conclusion: There was a considerably increased risk of postoperative AKI when intraoperative MAP was less than 55 mm Hg for more than 10 min. Strict blood pressure management is recommended even for patients younger than 60 years old

The Obesity-Related Dietary Pattern Is Associated with Higher Risk of Sleep Disorders: A Cross-Sectional Study from NHANES

Evidence on the association between dietary patterns and sleep disorders is limited and controversial. In addition, studies evaluating the effect of dietary patterns on sleep disorders have seldom considered the critical role of obesity. We aimed to explore obesity-related dietary patterns and evaluate their impact on sleep disorders using data from the National Health and Nutrition Examination Survey 2005–2014. In total, 19,892 participants aged over 20 years with two-day dietary recalls were enrolled. Obesity-related dietary patterns explaining most variance in waist circumference and BMI simultaneously were extracted from twenty-six food groups by the using partial least squares method. Sleep disorder and sleep duration, which were defined by self-reported questions, were the primary and the secondary outcome, respectively. Generalized linear models were performed to estimate the association of sleep disorders and sleep duration with dietary patterns. Two types of dietary patterns were identified. The “high fats, refined grains, and meat” pattern was characterized by high intakes of solid fats, cured meat, potatoes, refined grains, meat, cheese, and added sugars. The “low whole grains, vegetables, and fruits” pattern was characterized by low intakes of oils, whole grains, nuts and seeds, milk, fruits, and several vegetables. Participants with the highest adherence to the “high fats, refined grains, and meat” pattern had a higher risk for sleep disorders (OR (95%CI): 1.43 (1.12, 1.84)) and shorter sleep duration (β (95%CI): −0.17 (−0.26, −0.08)) compared to those with the lowest adherence. The corresponding associations for the “low whole grains, vegetables, and fruits” pattern were only significant for sleep duration (β (95%CI): −0.26 (−0.37, −0.15)). Our results found that the dietary pattern characterized by high solid fats, cured meat, potatoes, refined grains, meat, cheese, and added sugars, was associated with a higher risk for sleep disorders and shorter sleep duration

microRNA-dependent gene regulatory networks in maize leaf senescence

© 2016 Wu et al. Background: Maize grain yield depends mainly on the photosynthetic efficiency of functional leaves, which is controlled by an array of gene networks and other factors, including environmental conditions. MicroRNAs (miRNAs) are small RNA molecules that play important roles in plant developmental regulation. A few senescence-associated miRNAs (SA-miRNAs) have been identified as important participants in regulating leaf senescence by modulating the expression levels of their target genes. Results: To elucidate miRNA roles in leaf senescence and their underlying molecular mechanisms in maize, a stay-green line, Yu87-1, and an early leaf senescence line, Early leaf senescence-1 (ELS-1), were selected as experimental materials for the differential expression of candidate miRNAs. Four small RNA libraries were constructed from ear leaves at 20 and 30 days after pollination and sequenced by Illumina deep sequencing technology. Altogether, 81 miRNAs were detected in both lines. Of these, 16 miRNAs of nine families were differentially expressed between ELS-1 andYu87-1. The phenotypic and chlorophyll content analyses of both lines identified these 16 differentially expressed miRNAs as candidate SA-miRNAs. Conclusions: In this study, 16 candidate SA-miRNAs of ELS-1 were identified through small RNA deep sequencing technology. Degradome sequencing results indicated that these candidate SA-miRNAs may regulate leaf senescence through their target genes, mainly transcription factors, and potentially control chlorophyll degradation pathways. The results highlight the regulatory roles of miRNAs during leaf senescence in maize

Algae Bloom and Decomposition Changes the Phosphorus Cycle Pattern in Taihu Lake

Algae bloom event, an extreme ecological imbalance that the water environment experiences, changes the phosphorus (P) cycle in the aquatic environment, which makes the lake maintain a long-term eutrophication and frequent algae bloom state. This study compared P form characteristics and bacteria community structures in the aquatic environment of the cyanobacteria area and non-cyanobacteria area of Taihu Lake, aiming to clear the new P cycle pattern disturbed by algae bloom and decomposition processes. Compared with P forms in mediums of the middle of the lake and the east of the lake, there were higher concentration levels of total particulate P (TPP) in water, organic P (OP) in suspended particles, iron bound P (FeP) in sediments and phosphate (PO43−) in the pore water of Meiliang Bay, the cyanobacteria area. OP form was the dominant P fraction in suspended particles that occupied 69% in particulate total P, but OP proportion in sediments decreased to 26% of sediment total P, which indicated the strong occurrence of OP mineralization in sediments. The higher concentration and proportion of FeP in sediments of Meiliang Bay suggested the intensified effects of algae bloom and decomposition on sediment FeP accumulation. In Meiliang Bay, the positive correlation between Fe2+ and PO43− in pore water and the higher diffusion fluxes of Fe2+, PO43− from pore water to overlying water (0.45, 0.65 mg/m2·d) than that in the other lake areas also suggested the intensified effects of algae bloom and decomposition on FeP reductive dissolution in sediments accompanying sediment P remobilization. Moreover, there were higher concentrations of labile sulfide and high relative abundances of iron reducing bacteria (FRBs), sulfate reducing bacteria (SRBs) in sediments of Meiliang Bay. Results suggested that algae bloom event changed the natural P cycle in aquatic environment through intensifying the pathways of sediment OP mineralization, FeP accumulation and FeP reductive dissolution, which were mainly driven by the coupled factors of anoxic sediment condition, SRBs and FRBs activities. In addition, PO43− diffusion from pore water to overlying water in the east of the lake may be prevented for its much higher Fe/P ratio (8.06) and Fe2+ concentrations in pore water, which may form a P-adsorbing barrier of iron oxides in the interface between pore water and overlying water. This study enhances the understanding of the vicious P cycle pattern in the aquatic environment driven by algae bloom and decomposition, which should be considered when conducting eutrophication prevention and control measures on lakes

Differences in Carbon and Nitrogen Migration and Transformation Driven by Cyanobacteria and Macrophyte Activities in Taihu Lake

The metabolic activities of primary producers play an important role in the migration and transformation of carbon (C) and nitrogen (N) in aquatic environments. This study selected two typical areas in Taihu Lake, a cyanobacteria-dominant area (Meiliang Bay) and a macrophyte-dominant area (in the east area of the lake), to study the effects of cyanobacteria and macrophyte activities on C and N migration and transformation in aquatic environments. The results showed that total N and total particulate N concentrations in the water of the cyanobacteria-dominant area were much higher than those in the macrophyte-dominant area, which was mainly due to the assimilated intracellular N in cyanobacteria. Macrophyte activity drove a significantly higher release of dissolved organic C (DOC) in the water than that driven by cyanobacteria activity, and the DOC contents in the water of the macrophyte-dominant area were 2.4~4.6 times the DOC contents in the cyanobacteria-dominant area. In terms of the sediments, organic matter (OM), sediment total N and N species had positive correlations and their contents were higher in the macrophyte-dominant area than in the cyanobacteria-dominant area. Sediment OM contents in the macrophyte-dominant area increased from 4.19% to 9.33% as the sediment deepened (0~10 cm), while the opposite trend was presented in the sediments of the cyanobacteria-dominant area. Sediment OM in the macrophyte-dominant area may contain a relatively high proportion of recalcitrant OC species, while sediment OM in the cyanobacteria-dominant area may contain a relatively high proportion of labile OC species. Compared with the macrophyte-dominant area, there was a relatively high richness and diversity observed in the bacterial community in the sediments in the cyanobacteria-dominant area, which may be related to the high proportion of labile OC in the OM composition in its sediments. The relative abundances of most OC-decomposing bacteria, denitrifying bacteria, Nitrosomonas and Nitrospira were higher in the sediments of the cyanobacteria-dominant area than in the macrophyte-dominant area. These bacteria in the sediments of the cyanobacteria-dominant area potentially accelerated the migration and transformation of C and N, which may supply nutrients to overlying water for the demands of cyanobacteria growth. This study enhances the understanding of the migration and transformation of C and N and the potential effects of bacterial community structures under the different primary producer habitats

A hybrid deep learning and mechanistic kinetics model for the prediction of fluid catalytic cracking performance

© 2020 Institution of Chemical Engineers Fluid catalytic cracking (FCC) is one of the most important processes in the renewable energy as well as petrochemical industries. The prediction and understanding of the FCC performance in a real industrial environment is still challenging, as this is a highly complex process affected by many extremely non-linear and interrelated factors. In this paper, a novel hybrid predictive framework for FCC is developed by integrating a data-driven deep neural network with a physically meaningful lumped kinetic model, powered by orders of magnitude greater number of high-quality data from a modem automated FCC process. The results show that the novel hybrid model exhibits best predictions with regards to all the evaluation criteria such as Mean Absolute Percentage Error, Pearson coefficient, and standard deviation. It indicates that the hybrid data-driven deep learning with mechanistic kinetics model creates a better approach for fast prediction and optimization of complex reaction processes such as FCC