The effects of calcium on the expression of genes involved in ethylene biosynthesis and signal perception during tomato flower abscission

Calcium regulation of the genes involved in ethylene biosynthesis and ethylene receptors in flower abscission zones (AZ) of wild-type tomato (Lycopersicon esculentum Mill.) was investigated in this study. Calcium treatment delayed abscission of pedicel explants. However, verapamil (VP, calcium inhibitor) treatments accelerated abscission. Results showed an increase in AZ ethylene production consistent with abscission in both control and treatment explants. Moreover, in the AZ of flower pedicelstreated with VP, ethylene production, LeACO1 and LeETR1 expression was elevated, but LeETR4 expression decreased. Calcium treatments delayed the onset and maximum ethylene production, maintained low LeACO1 expression and elevated LeETR2, 3, 4, 5 expressions. Results also found that VP and calcium had little effect on LeACS2 expression. Our hypothesis that calcium is integral in preventing ethylene production at the point when 1-aminocyclopropane-1-carboxylic acid (ACC) is converted to ethylene was supported, but the study also revealed the regulation of expression in theethylene receptors. The potential roles of LeACO1, LeETR 3, 4, 5 and calcium in AZs during tomato pedicel explants abscission are discussed

Role of calcium in the expression of MAP kinase kinases (MKKs) and MAP kinases in tomato flower abscission

The mitogen activated protein kinase (MAPK) cascade is an important intracellular signaling module that functions as a convergent point for crosstalk during stress signaling. In this study, we constructed a phylogenetic tree for MAP kinase kinases (MKKs) and MAP kinases (MPKs) in Arabidopsis and Lycopersicon esculentum (tomato) and found that abscission-related AtMKK4 and AtMKK5, and AtMPK3 and AtMPK6 clustered with LeMKK2, and LeMPK1,and LeMPK2 and LeMPK3, respectively. To investigate whether there was a link between homology and abscission, we studied all the LeMKKs at the messenger ribonucleic acid (mRNA) transcript level. LeMKK2 and its homologue AtMKK4/5 both have roles in regulating abscission. Quantitative real time polymerase chain recation (qRT-PCR) of LeMPK1 and LeMPK2, which are highly homologous to AtMPK3 and AtMPK6, most probably mediate abscission downstream through LeMKK2. Calcium depressed the expression of these genes in the early stages of abscission. Treatment with W7 (a calmodulin inhibitor) indicated that the expression of LeMKKs and LeMPKs depended on calmodulin during tomato flower explant abscission.Keyword: Abscission, calcium, qRT-PCR, MAPK, tomat

Evaluation of the expression of internal control transcripts by real-time RT-PCR analysis during tomato flower abscission

Many investigations of the abscission mechanisms in plants are based on gene expression studies. Quantitative real-time RT-PCR (qRT-PCR) is widely and increasingly used for mRNA quantification, and the results are typically referenced to an internal control gene to avoid bias. We investigated the suitability of twelve tomato (Lycopersicon esculentum Mill. var. Liaoyuanduoli) housekeeping genes during hormone, high salt and temperature-induced abscission. The free software-based applications NormFinder and qBase PLUS were used to statistically identify the best internal controls for a given set of biological samples. The expression stability of a number of housekeeping genes were validated during tomato abscission. The two most suitable reference genes for the commonly used treatments of the major hormones related to abscission are TBP and RPL8. In some cases, more than three reference genes may be required, depending on the type of samples being compared. Four suitable reference genes (TBP/DNAj/RPL8/EXPRESSION) are recommended for more complex analysis, such as hormone and bio-stress induced abscission samples.Keywords: Abscission, housekeeping gene, Tomato, Lycopersicon esculentum, TBP, DNAj, RPL8, EXPRESSIO

Application of different watershed units to debris flow susceptibility mapping: A case study of Northeast China

The main purpose of this study was to compare two types of watershed units divided by the hydrological analysis method (HWUs) and mean curvature method (CWUs) for debris flow susceptibility mapping (DFSM) in Northeast China. Firstly, a debris flow inventory map consisting of 129 debris flows and 129 non-debris flows was randomly divided into a ratio of 70% and 30% for training and testing. Secondly, 13 influencing factors were selected and the correlations between these factors and the debris flows were determined by frequency ration analysis. Then, two types of watershed units (HWUs and CWUs) were divided and logistic regression (LR), multilayer perceptron (MLP), classification and regression tree (CART) and Bayesian network (BN) were selected as the evaluation models. Finally, the predictive capabilities of the models were verified using the predictive accuracy (ACC), the Kappa coefficient and the area under the receiver operating characteristic curve (AUC). The mean AUC, ACC and Kappa of four models (LR, MLP, CART and BN) in the training stage were 0.977, 0.931, and 0.861, respectively, for the HWUs, while 0.961, 0.905, and 0.810, respectively, for the CWUs; in the testing stage, were 0.904, 0.818, and 0.635, respectively, for the HWUs, while 0.883, 0.800, and 0.601, respectively, for the CWUs, which showed that HWU model has a higher debris flow prediction performance compared with the CWU model. The CWU-based model can reflect the spatial distribution probability of debris flows in the study area overall and can be used as an alternative model

IGFBP2 Plays an Essential Role in Cognitive Development during Early Life

Identifying the mechanisms underlying cognitive development in early life is a critical objective. The expression of insulin-like growth factor binding protein 2 (IGFBP2) in the hippocampus increases during neonatal development and is associated with learning and memory, but a causal connection has not been established. Here, it is reported that neurons and astrocytes expressing IGFBP2 are distributed throughout the hippocampus. IGFBP2 enhances excitatory inputs onto CA1 pyramidal neurons, facilitating intrinsic excitability and spike transmission, and regulates plasticity at excitatory synapses in a cell-type specific manner. It facilitates long-term potentiation (LTP) by enhancing N-methyl-d-aspartate (NMDA) receptor-dependent excitatory postsynaptic current (EPSC), and enhances neurite proliferation and elongation. Knockout of igfbp2 reduces the numbers of pyramidal cells and interneurons, impairs LTP and cognitive performance, and reduces tonic excitation of pyramidal neurons that are all rescued by IGFBP2. The results provide insight into the requirement for IGFBP2 in cognition in early life

Pollen tube emergence is mediated by ovary-expressed ALCATRAZ in cucumber

Pollen tube guidance within female tissues of flowering plants can be divided into preovular guidance, ovular guidance and a connecting stage called pollen tube emergence. As yet, no female factor has been identified to positively regulate this transition process. In this study, we show that an ovary-expressed bHLH transcription factor Cucumis sativus ALCATRAZ (CsALC) functions in pollen tube emergence in cucumber. CsALC knockout mutants showed diminished pollen tube emergence, extremely reduced entry into ovules, and a 95% reduction in female fertility. Further examination showed two rapid alkalinization factors CsRALF4 and CsRALF19 were less expressed in Csalc ovaries compared to WT. Besides the loss of male fertility derived from precocious pollen tube rupture as in Arabidopsis, Csralf4 Csralf19 double mutants exhibited a 60% decrease in female fertility due to reduced pollen tube distribution and decreased ovule targeting efficiency. In brief, CsALC regulates female fertility and promotes CsRALF4/19 expression in the ovary during pollen tube guidance in cucumber. Pollen tube growth is guided towards ovules. Here the authors show that a bHLH transcriptional factor CsALC functions in pollen tube emergence towards ovules to regulate female fertility in cucumber and promotes the expression of two rapid alkalinization factors CsRALF4/19 in the ovary

Deep learning-based polygenic risk analysis for Alzheimer's disease prediction

BACKGROUND: The polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk. METHODS: We constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model. RESULTS: The deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms. CONCLUSION: Our results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms

Research on Biological Detection Based on Reflected Light Images of a Porous Silicon Bragg Mirror

In this paper, based on the gray value change of reflected light image of porous silicon (PSi) Bragg mirror, a fast and simple biological detection method is proposed. In this method, CdSe/ZnS quantum dots (QDs) are used as markers for refractive index amplification, and digital image method is used for detection. The detection light has the same wavelength as the lowest reflectivity of the edge of the Bragg mirror, and the reflected light radiated on the surface of the Bragg mirror is received by the detector. The theoretical simulation results show that the intensity of the reflected light increases with the increase of the refractive index caused by the biological reaction. According to the experimental results, the average gray value variation increases with the increase of the target DNA concentration and becomes a linear relationship in a certain range. Based on this method, the DNA detection limit is 20.74 pM. The method is low-cost, has a short detection time and can be used in the detection of biosensor microarray

Laboratory Evaluation on Performance of Compound-Modified Asphalt for Rock Asphalt/Styrene–Butadiene Rubber (SBR) and Rock Asphalt/Nano-CaCO3

As a natural modifier of asphalt, rock asphalt has been widely used to improve its thermal stability and aging resistance. However, the thermal cracking resistance of asphalt modified by rock asphalt is unsatisfactory. In order to improve the thermal cracking resistance in low temperature, two kinds of modifiers—styrene–butadiene rubber (SBR) and nano-CaCO3—were selected as the compound modifiers, and then implemented to improve the low-temperature performance of the binder. Then, compound asphalt modified by Buton rock asphalt (BRA) was chosen as the study subject. The thermal stability and aging resistance of asphalt modified by BRA, compound-modified asphalt by BRA/SBR, and compound-modified asphalt by BRA and nano-CaCO3 were determined to identify whether the compound modifiers in the asphalt would have a negative effect on the thermal stability and aging resistance of the asphalt. The dynamic shear rheometer (DSR) test was employed to evaluate the thermal stability. The thin film oven test (TFOT) and pressure aging vessel (PAV) were adopted to determine the aging resistance. The viscoelastic characteristics of asphalt with and without modifiers were revealed to evaluate the low-temperature crack resistance of asphalt modified by compound modifiers. The bending beam rheometer (BBR) creep test was conducted in three test temperatures in order to determine the creep stiffness modulus of the BRA compound-modified asphalt. The viscoelastic model considering the damage caused by loading was established; then, the creep compliance and parameters of the viscoelastic damage model were implemented to evaluate the low-temperature performance of the compound-modified asphalt. The results show that the compound modifiers have little negative effects on the thermal stability and aging resistance of asphalt. The thermal crack resistance of the compound-modified asphalt by BRA/SBR was the best, followed by the compound-modified asphalt by BRA and nano-CaCO3 within the three materials. The accuracy of forecasting the characteristics of compound-modified asphalt was improved by using the viscoelastic model and considering the damage effect

Rapid determination of tannin in Danshen and Guanxinning injections using UV spectrophotometry for quality control

A technique for the determination of tannin content in traditional Chinese medicine injections (TCMI) was developed based on ultraviolet (UV) spectroscopy. Chemometrics were used to construct a mathematical model of absorption spectrum and tannin reference content of Danshen and Guanxinning injections, and the model was verified and applied. The results showed that the established UV-based spectral partial least squares regression (PLS) tannin content model performed well with a correlation coefficient (r) of 0.952, root mean square error of calibration (RMSEC) of 0.476μg/ml, root mean square error of validation (RMSEV) of 1.171μg/ml, and root mean square error of prediction (RMSEP) of 0.465μg/ml. Pattern recognition models using linear discriminant analysis (LDA) and k nearest neighbor (k-NN) classifiers based on UV spectrum could successfully classify different types of injections and different manufacturers. The established method to measure tannin content based on UV spectroscopy is simple, rapid and reliable and provides technical support for quality control of tannin in Chinese medicine injections