Steroid hormone ecdysone deficiency stimulates preparation for photoperiodic reproductive diapause.

Diapause, a programmed developmental arrest primarily induced by seasonal environmental changes, is very common in the animal kingdom, and found in vertebrates and invertebrates alike. Diapause provides an adaptive advantage to animals, as it increases the odds of surviving adverse conditions. In insects, individuals perceive photoperiodic cues and modify endocrine signaling to direct reproductive diapause traits, such as ovary arrest and increased fat accumulation. However, it remains unclear as to which endocrine factors are involved in this process and how they regulate the onset of reproductive diapause. Here, we found that the long day-mediated drop in the concentration of the steroid hormone ecdysone is essential for the preparation of photoperiodic reproductive diapause in Colaphellus bowringi, an economically important cabbage beetle. The diapause-inducing long-day condition reduced the expression of ecdysone biosynthetic genes, explaining the drop in the titer of 20-hydroxyecdysone (20E, the active form of ecdysone) in female adults. Application of exogenous 20E induced vitellogenesis and ovarian development but reduced fat accumulation in the diapause-destined females. Knocking down the ecdysone receptor (EcR) in females destined for reproduction blocked reproductive development and induced diapause traits. RNA-seq and hormone measurements indicated that 20E stimulates the production of juvenile hormone (JH), a key endocrine factor in reproductive diapause. To verify this, we depleted three ecdysone biosynthetic enzymes via RNAi, which confirmed that 20E is critical for JH biosynthesis and reproductive diapause. Importantly, impairing Met function, a component of the JH intracellular receptor, partially blocked the 20E-regulated reproductive diapause preparation, indicating that 20E regulates reproductive diapause in both JH-dependent and -independent manners. Finally, we found that 20E deficiency decreased ecdysis-triggering hormone signaling and reduced JH production, thereby inducing diapause. Together, these results suggest that 20E signaling is a pivotal regulator that coordinates reproductive plasticity in response to environmental inputs.

GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest.

Predicting disease-related long non-coding RNAs (lncRNAs) is beneficial to finding of new biomarkers for prevention, diagnosis and treatment of complex human diseases. In this paper, we proposed a machine learning techniques-based classification approach to identify disease-related lncRNAs by graph auto-encoder (GAE) and random forest (RF) (GAERF). First, we combined the relationship of lncRNA, miRNA and disease into a heterogeneous network. Then, low-dimensional representation vectors of nodes were learned from the network by GAE, which reduce the dimension and heterogeneity of biological data. Taking these feature vectors as input, we trained a RF classifier to predict new lncRNA-disease associations (LDAs). Related experiment results show that the proposed method for the representation of lncRNA-disease characterizes them accurately. GAERF achieves superior performance owing to the ensemble learning method, outperforming other methods significantly. Moreover, case studies further demonstrated that GAERF is an effective method to predict LDAs.

Rhamnosyltransferases involved in the biosynthesis of flavone rutinosides in Chrysanthemum species.

Linarin (acacetin-7-O-rutinoside), isorhoifolin (apigenin-7-O-rutinoside), and diosmin (diosmetin-7-O-rutinoside) are chemically and structurally similar flavone rutinoside (FR) compounds found in Chrysanthemum L. (Anthemideae, Asteraceae) plants. However, their biosynthetic pathways remain largely unknown. In this study, we cloned and compared FRs and genes encoding rhamnosyltransferases (RhaTs) among eight accessions of Chrysanthemum polyploids. We also biochemically characterized RhaTs of Chrysanthemum plants and Citrus (Citrus sinensis and Citrus maxima). RhaTs from these two genera are substrate-promiscuous enzymes catalyzing the rhamnosylation of flavones, flavanones, and flavonols. Substrate specificity analysis revealed that Chrysanthemum 1,6RhaTs preferred flavone glucosides (e.g. acacetin-7-O-glucoside), whereas Cs1,6RhaT preferred flavanone glucosides. The nonsynonymous substitutions of RhaTs found in some cytotypes of diploids resulted in the loss of catalytic function. Phylogenetic analysis and specialized pathways responsible for the biosynthesis of major flavonoids in Chrysanthemum and Citrus revealed that rhamnosylation activity might share a common evolutionary origin. Overexpression of RhaT in hairy roots resulted in 13-, 2-, and 5-fold increases in linarin, isorhoifolin, and diosmin contents, respectively, indicating that RhaT is mainly involved in the biosynthesis of linarin. Our findings not only suggest that the substrate promiscuity of RhaTs contributes to the diversity of FRs in Chrysanthemum species but also shed light on the evolution of flavone and flavanone rutinosides in distant taxa.

MiRNA-disease association prediction via hypergraph learning based on high-dimensionality features.

BACKGROUND: MicroRNAs (miRNAs) have been confirmed to have close relationship with various human complex diseases. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases. However, it is still a big challenge to identify which miRNAs are related to diseases. As experimental methods are in general expensive and time-consuming, it is important to develop efficient computational models to discover potential miRNA-disease associations. METHODS: This study presents a novel prediction method called HFHLMDA, which is based on high-dimensionality features and hypergraph learning, to reveal the association between diseases and miRNAs. Firstly, the miRNA functional similarity and the disease semantic similarity are integrated to form an informative high-dimensionality feature vector. Then, a hypergraph is constructed by the K-Nearest-Neighbor (KNN) method, in which each miRNA-disease pair and its k most relevant neighbors are linked as one hyperedge to represent the complex relationships among miRNA-disease pairs. Finally, the hypergraph learning model is designed to learn the projection matrix which is used to calculate uncertain miRNA-disease association score. RESULT: Compared with four state-of-the-art computational models, HFHLMDA achieved best results of 92.09% and 91.87% in leave-one-out cross validation and fivefold cross validation, respectively. Moreover, in case studies on Esophageal neoplasms, Hepatocellular Carcinoma, Breast Neoplasms, 90%, 98%, and 96% of the top 50 predictions have been manually confirmed by previous experimental studies. CONCLUSION: MiRNAs have complex connections with many human diseases. In this study, we proposed a novel computational model to predict the underlying miRNA-disease associations. All results show that the proposed method is effective for miRNA-disease association predication.

Graph regularized L2,1-nonnegative matrix factorization for miRNA-disease association prediction.

BACKGROUND: The aberrant expression of microRNAs is closely connected to the occurrence and development of a great deal of human diseases. To study human diseases, numerous effective computational models that are valuable and meaningful have been presented by researchers. RESULTS: Here, we present a computational framework based on graph Laplacian regularized L2, 1-nonnegative matrix factorization (GRL2, 1-NMF) for inferring possible human disease-connected miRNAs. First, manually validated disease-connected microRNAs were integrated, and microRNA functional similarity information along with two kinds of disease semantic similarities were calculated. Next, we measured Gaussian interaction profile (GIP) kernel similarities for both diseases and microRNAs. Then, we adopted a preprocessing step, namely, weighted K nearest known neighbours (WKNKN), to decrease the sparsity of the miRNA-disease association matrix network. Finally, the GRL2,1-NMF framework was used to predict links between microRNAs and diseases. CONCLUSIONS: The new method (GRL2, 1-NMF) achieved AUC values of 0.9280 and 0.9276 in global leave-one-out cross validation (global LOOCV) and five-fold cross validation (5-CV), respectively, showing that GRL2, 1-NMF can powerfully discover potential disease-related miRNAs, even if there is no known associated disease.

Curcumin rescues aging-related loss of hippocampal synapse input specificity of long term potentiation in mice.

Curcumin has neuroprotective effect and could enhance memory. However, the mechanisms underlying the protection of curcumin on aging-related memory decline are not well understood. In this study, high frequency stimulation (HFS)-induced long term potentiation (LTP) was evaluated by a cellular model of memory formation. A two-input stimulation paradigm was used to record the potentiation as well as synapse input specificity. The data suggested that an N-Methyl-D-aspartate receptors (NMDAR) -dependent LTP was inducible in adult hippocampal slices with a characteristic of synapse input specificity. It also indicated that aging resulted in a reduction in LTP but more importantly a loss of synaptic input specificity. The reason behind the above conclusions is that LTP induction is more dependent on the calcium channel. This is due to a switch of the dependence of LTP induction to voltage-dependent calcium channel (VDCC) compared to NMDA receptors. Curcumin administration recovers input specificity by re-establishing NMDA receptor dependence of induction. In addition, curcumin administration ameliorated aging-related increase of brain thiobarbituric acid-reactive substances and elevated aging-related decrease of glutathione in hippocampus. It is then concluded that curcumin modulates hippocampal redox status and restores aging-related loss of synapse input specificity of HFS-induced LTP by switching VDCC calcium source into NMDA receptor-dependent one.

A New Method Based on Matrix Completion and Non-Negative Matrix Factorization for Predicting Disease-Associated miRNAs.

Numerous studies have shown that microRNAs are associated with the occurrence and development of human diseases. Thus, studying disease-associated miRNAs is significantly valuable to the prevention, diagnosis and treatment of diseases. In this paper, we proposed a novel method based on matrix completion and non-negative matrix factorization (MCNMF)for predicting disease-associated miRNAs. Due to the information inadequacy on miRNA similarities and disease similarities, we calculated the latter via two models, and introduced the Gaussian interaction profile kernel similarity. In addition, the matrix completion (MC)was employed to further replenish the miRNA and disease similarities to improve the prediction performance. And to reduce the sparsity of miRNA-disease association matrix, the method of weighted K nearest neighbor (WKNKN)was used, which is a pre-processing step. We also utilized non-negative matrix factorization (NMF)using dual L2,1-norm, graph Laplacian regularization, and Tikhonov regularization to effectively avoid the overfitting during the prediction. Finally, several experiments and a case study were implemented to evaluate the effectiveness and performance of the proposed MCNMF model. The results indicated that our method could reliably and effectively predict disease-associated miRNAs.

Extra Trees Method for Predicting LncRNA-Disease Association Based On Multi-Layer Graph Embedding Aggregation.

Lots of experimental studies have revealed the significant associations between lncRNAs and diseases. Identifying accurate associations will provide a new perspective for disease therapy. Calculation-based methods have been developed to solve these problems, but these methods have some limitations. In this paper, we proposed an accurate method, named MLGCNET, to discover potential lncRNA-disease associations. Firstly, we reconstructed similarity networks for both lncRNAs and diseases using top k similar information, and constructed a lncRNA-disease heterogeneous network (LDN). Then, we applied Multi-Layer Graph Convolutional Network on LDN to obtain latent feature representations of nodes. Finally, the Extra Trees was used to calculate the probability of association between disease and lncRNA. The results of extensive 5-fold cross-validation experiments show that MLGCNET has superior prediction performance compared to the state-of-the-art methods. Case studies confirm the performance of our model on specific diseases. All the experiment results prove the effectiveness and practicality of MLGCNET in predicting potential lncRNA-disease associations.

[Spectral line shift property of prism dispersive imaging spectrometer].

In order to study the spectral line shift property of prism-dispersive imaging spectrometer, the influencing factors and mechanisms of spectral line shift were presented, and the mathematical model based on linear optics model was established to describe the spectral line shift property. Code V API functions was used, in Matlab environment, to verify the validity of mathematical model, and the sensitivity coefficient of spectral line shift was analyzed. Results indicate that rigid body motion of optical mirror surface generated by environmental variation is the key causation of spectral line shift. When the decenter of mirror surface is no more than 0.2 mm and the tilt is less than 0.02 degrees, the value of spectral line shift of different wavelengths at different fields is equivalent, and the error is less than 0.1 pixel. Spectral line shift due to mirror rigid body motion is linear and independent, and the total shift of the spectral line is the algebraic sum of values produced by the single freedom of motion (DOF) of single mirror surface. The mathematical model based on linear optics model can be used to study the spectral line shift property of the prism-dispersive imaging spectrometer. It will provide some guidance for spectral calibration and spectral property analysis under complex work condition.

Krüppel homolog 1 regulates photoperiodic reproductive plasticity in the cabbage beetle Colaphellus bowringi.

Many insects exhibit reproductive plasticity where the photoperiod determines whether the insect becomes reproductively active or enters diapause. Adult reproductive diapause is a strategy that allows insects to survive harsh environmental conditions. A deficiency in juvenile hormone (JH) leads to reproductive diapause. However, little is known about the molecular mechanisms by which JH signaling regulates reproductive diapause. In this study, we used the cabbage beetle Colaphellus bowringi, a serious pest, to investigate the role of Krüppel homolog 1 (Kr-h1) in controlling photoperiodic plasticity of female reproduction. We focused on Kr-h1, since it acts as a key mediator of JH signaling. We show here that JH-Methoprene-tolerant signaling upregulated the expression of Kr-h1 in reproductively active C. bowringi females when reared under short day conditions. In the long day-treated diapausing females, Kr-h1 transcripts decreased dramatically. Interfering with Kr-h1 function repressed reproductive development by blocking vitellogenesis and ovarian growth. Further, Kr-h1 depletion induced other diapause-like traits, including elevated lipid accumulation and high expression of diapause-related genes. RNA-Seq showed that Kr-h1 played both activating and repressive roles, depending on whether downstream genes were acting in reproduction- or diapause pathways, respectively. Finally, we identified the DNA replication gene mini-chromosome maintenance 4 and two triacylglycerol lipase genes as critical downstream factors of Kr-h1 that are critical for reproductive plasticity in C. bowringi. These results reveal that Kr-h1 is a key component of the regulatory pathway that coordinates reproduction and diapause in insects in response to photoperiodic input.

[Stray light of space-borne hyperspectral imager and its measurement].

Hyperspectral imager has more spectral channels and higher spectral resolution compared to normal spectral instrument. Stray light is one of the important reasons affecting the spectral accuracy of the hyperspectral imager, but the current stray light measurements can't meet the need. Definition, sources and harmfulness of stray light are described in the present paper. The feasibility and superiority of the stray light factor d(i,j), used to describe the stray light characteristics of the spectral instruments, is investigated thoroughly, where the definition, physical significance and value of engineering application of stray light interference factor fi(lamda) and stray light disturbance factor Fi(lamda) are given. Finally, the system components, measurement procedure and measurement results of the stray light measurement system, using narrow-band filter, are introduced. Results show that the stray light factor d(i,j) meets the need of the stray light measurement because it can indicate the stray light characteristics of the spectral instruments without relationship to the light source, filter, detector and other measurement conditions, and the measurement efficiency is increased at least one-fold than the spectral stray light factors method.

[Thermal spectral property of prism in hyper spectral imager].

Prism is one of the most key parts in the hyper spectral imager (HSI). Consequently, to set thermal control target and make thermal control design, the thermal spectral property of prism in the HSI was studied. The working principle of the HSI and the definition of its thermal spectral property were introduced. The working environment of prism and its thermal effect were analyzed; also the study contents and technical route of the prism's thermal spectral property were discussed. The effects of different uniform temperature field on deflexion angle and angular dispersion of the prism in the HSI were deduced, and the changes in displacement of the spectra and the spectral bandwidth under different uniform temperature were obtained. For one instance, the thermal spectral property of the K9 prism and the fused silica prism were compared based on FEM and combined experiments, furthermore, its thermal control target was ascertained and a thermal spectral property test was carried out to validate the rationality of the thermal spectral property analysis. The results of analysis indicated that the changes in spectral bandwidth and spectrum resolution brought by thermal distortions can be ignored according to current fixing mode, and the displacement of the spectra is mainly determined by thermal coefficient of material refractive index; because of it's the lower thermal coefficient of material refractive index, the displacement of the spectra of the K9 prism is smaller under the same temperature changes; the material deflexion changes (dn/dlambda) of prism are not sensitive to the temperature, so the changes in spectral bandwidth caused by them are not obvious. And the results of test proved that the studied method of thermal spectral property is reasonable and essential, and the results are authentic and credible. So it can provide some guidance for setting thermal control target and optimizing thermal control design.

Correlation between macroscopic characteristics and tissue-specific chemical profiling of the root of Salvia miltiorrhiza.

BACKGROUND: Macroscopic identification has been widely used as a convenient method for herbal authentication and quality assessment. However, sensory evaluation heavily relied on personal experience and lacked enough evidence-based validations. PURPOSE: We aim to reveal the correlation between macroscopic characteristics and tissue-specific chemical composition of the root of Salvia miltiorrhiza (SMR), and then develop a rapid method for quality assessment. METHODS: Thirty-two batches of SMR were collected and evaluated. The outer-surface color and diameter as the representative tissue features of SMR were selected as the macroscopic indexes. SMR were then divided into three parts along transverse section as outer bark, middle part and central part, to explore the spatial distribution of chemicals. Outer-surface color information was converted into RGB values, while the diameter data were expressed by mean distance, respectively. Thirteen major components including eight salvianolic acids and five tanshinones in each part were determined by liquid chromatography tandem mass spectrometry. Finally, several mathematical models were established and optimized to evaluate the correlation between outer-surface color, size and chemical distribution. RESULT: All five tanshinones mainly distributed in the outer bark while salvianolic acids were averagely existed among three parts. Correlational studies revealed that the surface color depth was significantly and positively correlated with tanshinone contents in the outer bark, while the size showed poor correlation in any chemicals. A color-oriented model was thus developed for the prediction of tanshinone contents in SMR, and a 9 × 9 standard color chart was created for easily use. CONCLUSION: This study contributes an alternative method for macroscopic features-based quality evaluation of herbs, and also complements some scientific data for traditional knowledge.

GPR30 Activation Contributes to the Puerarin-Mediated Neuroprotection in MPP+-Induced SH-SY5Y Cell Death.

The neuroprotective action of puerarin in Parkinson's disease (PD) models has been well investigated. However, the mechanisms involved in protection have not been completely understood. G protein-coupled receptor 30 (GPR30) is a G protein-coupled estrogen receptor and considered a potential target in the neuroprotection against PD. In this study, we investigated whether puerarin prevented against 1-methyl-4-phenylpyridinium (MPP+)-induced cell death via GPR30. Our results showed that the GPR30 agonist, G1, exhibited puerarin-mediated neuroprotection against MPP+-induced cell death of SH-SY5Y cells. This protective action was reversed by the GPR30 antagonist. Moreover, a time- and concentration-dependent effect of puerarin on GPR30 expression was verified at the protein level but not at the mRNA level. Further, we showed that an mTor-dependent new GPR30 synthesis contributed to the protection conferred by puerarin. Finally, glial cell line-derived neurotrophic factor (GDNF) levels were enhanced by puerarin and G1 in both control and MPP+-lesioned cells via GPR30. Taken together, our data strongly suggest that puerarin prevents MPP+-induced cell death via facilitating GPR30 expression and GDNF release.

[Sorption Characteristics of Phenanthrene and 1, 1-Dichloroethene onto Reed Straw Biochar in Aquatic Solutions].

The purpose of this study was to investigate the sorption characteristics of phenanthrene (PHE) and 1, 1-dichloroethene (1, 1-DCE) onto reed straw biochar at 500 degrees C in aquatic solutions. The sorption mechanisms and effects of solution pH and biochar mass on sorption intensity were discussed. The results showed that the time required to reach sorption equilibrium was 60 min and 480 min for PHE and 1, 1-DCE, respectively, with maximum removal rates of 81, 87% and 90.18%. The sorption kinetics of both PHE and 1, 1-DCE fitted the pseudo-second-order model well, but the pseudo-second-order reaction rate of PHE was higher than that of 1, 1-DCE. Furthermore, the sorption processes were controlled by both membrane diffusion and intra-particle diffusion, and the latter was found to be the rate-controlling step. Sorption isotherms of the two organic pollutants fitted well with the Freundlich equation, and the sorption affinity of 1, 1-DCE onto biochar was greater than that of PHE. The total sorption mechanism of biochar was the combination of partition and adsorption, and dominated by adsorption. The adsorption capacity of 1, 1-DCE was greater than that of PHE, but its partition capacity was much smaller, indicating that pollutants' molecular volume and relative polarity would mainly affect the total sorption. Analysis of Fourier transform infrared spectroscopy (FTIR) demonstrated that oxygen- and hydrogen-containing functional groups and pi--pi interaction were important for PHE and 1, 1-DCE sorption onto biochar. The solution pH value had no significant effect on the sorption intensity of PHE and 1, 1-DCE, however, with biochar mass increasing from 5 mg to 50 mg, the equilibrium sorption amount of PHE and 1, 1-DCE decreased by 6.78 times and 2.18 times, and the removal rate increased by 20.21% and 15.78%, respectively.