The roles of microRNAs in horticultural plant disease resistance

The development of the horticultural industry is largely limited by disease and excessive pesticide application. MicroRNAs constitute a major portion of the transcriptomes of eukaryotes. Various microRNAs have been recognized as important regulators of the expression of genes involved in essential biological processes throughout the whole life cycle of plants. Recently, small RNA sequencing has been applied to study gene regulation in horticultural plants. In this review, we summarize the current understanding of the biogenesis and contributions of microRNAs in horticultural plant disease resistance. These microRNAs may potentially be used as genetic resources for improving disease resistance and for molecular breeding. The challenges in understanding horticultural plant microRNA biology and the possibilities to make better use of these horticultural plant gene resources in the future are discussed in this review

QTLs and candidate genes analyses for fruit size under domestication and differentiation in melon (Cucumis melo L.) based on high resolution maps

Background: Melon is a very important horticultural crop produced worldwide with high phenotypic diversity. Fruit size is among the most important domestication and differentiation traits in melon. The molecular mechanisms of fruit size in melon are largely unknown. Results: Two high-density genetic maps were constructed by whole-genome resequencing with two F2 segregating populations (WAP and MAP) derived from two crosses (cultivated agrestis × wild agrestis and cultivated melo × cultivated agrestis). We obtained 1,871,671 and 1,976,589 high quality SNPs that show differences between parents in WAP and MAP. A total of 5138 and 5839 recombination events generated 954 bins in WAP and 1027 bins in MAP with the average size of 321.3 Kb and 301.4 Kb respectively. All bins were mapped onto 12 linkage groups in WAP and MAP. The total lengths of two linkage maps were 904.4 cM (WAP) and 874.5 cM (MAP), covering 86.6% and 87.4% of the melon genome. Two loci for fruit size were identified on chromosome 11 in WAP and chromosome 5 in MAP, respectively. An auxin response factor and a YABBY transcription factor were inferred to be the candidate genes for both loci. Conclusion: The high-resolution genetic maps and QTLs analyses for fruit size described here will provide a better understanding the genetic basis of domestication and differentiation, and provide a valuable tool for map-based cloning and molecular marker assisted breeding.info:eu-repo/semantics/publishedVersio

Prognostic effect of atrial fibrillation on survival in patients with hypertrophic cardiomyopathy: a meta-analysis

Abstract Objective To systematically evaluate the prognostic impact of atrial fibrillation (AF) in patients with hypertrophic cardiomyopathy (HCM). Methods The Chinese and English databases (PubMed, EMBASE, Cochrane Library, Chinese National Knowledge Infrastructure, and Wanfang database were systematically searched to include observational studies on the prognosis of AF in cardiovascular events or death in patients with HCM; these were evaluated using Revman 5.3. Results After systematic search and screening, a total of 11 studies with a high study quality were included in this study. Meta-analysis showed that patients with HCM accompanied by AF had a higher risk of all-cause death (odds ratio [OR] = 2.75; 95% confidence interval [CI]: 2.18–3.47; P < 0.001), heart-related death (OR = 2.62; 95%CI: 2.02–3.40; P < 0.001), sudden cardiac death (OR = 7.09; 95%CI: 5.77–8.70; P < 0.001), heart-failure-related death (OR = 2.04; 95%CI: 1.24–3.36; P = 0.005), and stroke death (OR = 17.05; 95%CI: 6.99–41.58; P < 0.001) compared with patients with HCM without AF. Conclusion Atrial fibrillation is a risk factor for adverse survival outcomes in patients with HCM, and aggressive interventions are needed in this population to avoid the occurrence of adverse outcomes

Enhancing interfacial strength between AA5083 and cryogenic adhesive via anodic oxidation and silanization

AA5083 aluminum alloy was treated in turn with phosphoric-sulfuric acid anodic oxidation and then with silanization using the silane coupling agent KH560. A chemical bond (Si-O-Al) was created between the aluminum alloy and silane film, and a dehydration condensation reaction occurred between the silane film and cryogenic adhesive to enhance the bonding strength between the aluminum alloy and the cryogenic adhesive. Scanning electron microscopy, Energy dispersive spectroscopy, and Fourier transform infrared spectroscopy were used to explore the interfacial characteristics of the aluminum alloy both with and without the applied treatment. Furthermore, single lap shear tests and durability tests were performed to assess the adhesive strength of the interface between the aluminum alloy and the cryogenic adhesive at low temperature. The most improved interfacial strength using the anodic oxidation and the silanization treatments reached 33.96 MPa at −60 °C. The interface strength with the same treatments after the durability test was 25.4 MPa

Plasma miR-22-5p, miR-132-5p, and miR-150-3p Are Associated with Acute Myocardial Infarction

Circulating microRNAs (miRNAs) are potential biomarkers for cardiovascular diseases. Our study aimed to determine whether miR-22-5p, miR-132-5p, and miR-150-3p represent novel biomarkers for acute myocardial infarction (AMI). Plasma samples were isolated from 35 AMI patients and 55 matched controls. Total RNA was extracted, and quantitative real-time PCR and ELISA were performed to investigate the expressions of miRNAs and cardiac troponin I (cTnI), respectively. We found that plasma levels of miR-22-5p and miR-150-3p were significantly higher during the early stage of AMI and their expression levels peaked earlier than cTnI. Conversely, circulating miR-132-5p was sustained at a low level during the early phase of AMI. All three circulating miRNAs were correlated with plasma cTnI levels. A receiver operating characteristic (ROC) analysis suggested that each single miRNA had considerable diagnostic efficacy for AMI. Moreover, combining the three miRNAs improved their diagnostic efficacy. Furthermore, neither heparin nor medications for coronary heart disease (CHD) affected plasma levels of miR-22-5p and miR-132-5p, but circulating miR-150-3p was downregulated by medications for CHD. We concluded that plasma miR-22-5p, miR-132-5p, and miR-150-3p may serve as candidate diagnostic biomarkers for early diagnosis of AMI. Moreover, a panel consisting of these three miRNAs may achieve a higher diagnostic value

The Simultaneous Prediction of Soil Properties and Vegetation Coverage from Vis-NIR Hyperspectral Data with a One-Dimensional Convolutional Neural Network: A Laboratory Simulation Study

Remote sensing of land surface mostly obtains a mixture of spectral information of soil and vegetation. It is thus of great value if soil and vegetation information can be acquired simultaneously from one model. In this study, we designed a laboratory experiment to simulate land surface compositions, including various soil types with varying soil moisture and vegetation coverage. A model of a one-dimensional convolutional neural network (1DCNN) was established to simultaneously estimate soil properties (organic matter, soil moisture, clay, and sand) and vegetation coverage based on the hyperspectral data measured in the experiment. The results showed that the model achieved excellent predictions for soil properties (R2 = 0.88&ndash;0.91, RPIQ = 4.01&ndash;5.78) and vegetation coverage (R2 = 0.95, RPIQ = 7.75). Compared with the partial least-squares regression (PLSR), the prediction accuracy of 1DCNN improved 42.20%, 45.82%, 43.32%, and 36.46% in terms of the root-mean-squared error (RMSE) for predicting soil organic matter, sand, clay, and soil moisture, respectively. The improvement might be caused by the fact that the spectral preprocessing and spectral features useful for predicting soil properties were successfully identified in the 1DCNN model. For the prediction of vegetation coverage, although the prediction accuracy by 1DCNN was excellent, its performance (R2 = 0.95, RPIQ = 7.75, RMSE = 3.92%) was lower than the PLSR model (R2 = 0.98, RPIQ = 12.57, RMSE = 2.41%). These results indicate that 1DCNN can simultaneously predict soil properties and vegetation coverage. However, the factors such as surface roughness and vegetation type that could affect the prediction accuracy should be investigated in the future

The Simultaneous Prediction of Soil Properties and Vegetation Coverage from Vis-NIR Hyperspectral Data with a One-Dimensional Convolutional Neural Network: A Laboratory Simulation Study

Remote sensing of land surface mostly obtains a mixture of spectral information of soil and vegetation. It is thus of great value if soil and vegetation information can be acquired simultaneously from one model. In this study, we designed a laboratory experiment to simulate land surface compositions, including various soil types with varying soil moisture and vegetation coverage. A model of a one-dimensional convolutional neural network (1DCNN) was established to simultaneously estimate soil properties (organic matter, soil moisture, clay, and sand) and vegetation coverage based on the hyperspectral data measured in the experiment. The results showed that the model achieved excellent predictions for soil properties (R2 = 0.88–0.91, RPIQ = 4.01–5.78) and vegetation coverage (R2 = 0.95, RPIQ = 7.75). Compared with the partial least-squares regression (PLSR), the prediction accuracy of 1DCNN improved 42.20%, 45.82%, 43.32%, and 36.46% in terms of the root-mean-squared error (RMSE) for predicting soil organic matter, sand, clay, and soil moisture, respectively. The improvement might be caused by the fact that the spectral preprocessing and spectral features useful for predicting soil properties were successfully identified in the 1DCNN model. For the prediction of vegetation coverage, although the prediction accuracy by 1DCNN was excellent, its performance (R2 = 0.95, RPIQ = 7.75, RMSE = 3.92%) was lower than the PLSR model (R2 = 0.98, RPIQ = 12.57, RMSE = 2.41%). These results indicate that 1DCNN can simultaneously predict soil properties and vegetation coverage. However, the factors such as surface roughness and vegetation type that could affect the prediction accuracy should be investigated in the future

Genome-Wide Identification of Reference Genes for Reverse-Transcription Quantitative PCR in Goat Rumen

As the largest chamber of the ruminant stomach, the rumen not only serves as the principal absorptive surface and nutrient transport pathway from the lumen into the animal, but also plays an important short-chain fatty acid (SCFA) metabolic role in addition to protective functions. Accurate characterization of the gene expression profiles of genes of interest is essential to the exploration of the intrinsic regulatory mechanisms of rumen development in goats. Thus, the selection of suitable reference genes (RGs) is an important prerequisite for real-time quantitative PCR (RT-qPCR). In the present study, 16 candidate RGs were identified from our previous transcriptome sequencing of caprine rumen tissues. The quantitative expressions of the candidate RGs were measured using the RT-qPCR method, and the expression stability of the RGs was assessed using the geNorm, NormFinder, and BestKeeper programs. GeNorm analysis showed that the M values were less than 0.5 for all the RGs except GAPT4, indicating that they were stably expressed in the rumen tissues throughout development. RPS4X and RPS6 were the two most stable RGs. Furthermore, the expressions of two randomly selected target genes (IGF1 and TOP2A), normalized by the selected most stable RGs (RPS4X and RPS6), were consistent with the results of RNA sequencing, while the use of GAPDH and ACTB as RGs resulted in altered profiles. Overall, RPS4X and RPS6 showed the highest expression stability and the lowest coefficients of variation, and could be used as the optimal reference combination for quantifying gene expression in rumen tissues via RT-qPCR analysis

Robust single-sideband-modulated Raman light generation for atom interferometry by FBG-based optical rectangular filtration

Low-phase-noise and pure-spectrum Raman light is vital for high-precision atom interferometry by two-photon Raman transition. A preferred and prevalent solution for Raman light generation is electro-optic phase modulation. However, phase modulation inherently brings in double sidebands, resulting in residual sideband effects of multiple laser pairs beside Raman light in atom interferometry. Based on a well-designed rectangular fiber Bragg grating and an electro-optic modulator, optical single-sideband modulation has been realized at 1560 nm with a stable suppression ratio better than -25 dB despite of intense temperature variations. After optical filtration and frequency doubling, a robust phase-coherent Raman light at 780 nm is generated with a stable SNR of better than -19 dB and facilitates measuring the local gravity successfully. This proposed all-fiber single-sideband-modulated Raman light source, characterized as robust, compact and low-priced, is practical and potential for field applications of portable atom interferometry.Comment: 12 pages, 7 figure