The Steroid Hormone 20-Hydroxyecdysone Promotes the Cytoplasmic Localization of Yorkie to Suppress Cell Proliferation and Induce Apoptosis.

The transcriptional co-activator Yki (Yorkie), a member of the Hippo pathway, regulates cell proliferation or apoptosis, depending on its nuclear or cytoplasmic location. However, the upstream factors regulating the subcellular localization of Yki are unclear. We found that the steroid hormone 20-hydroxyecdysone (20E) induces phosphorylation of Yki, causing it to remain in the cytoplasm, where it promotes apoptosis in the midgut of the lepidopteran insect Helicoverpa armigera Yki is expressed in various tissues, with an increase in the epidermis and midgut during early metamorphic molting. Yki is localized mainly in the nucleus of feeding larval midgut cells but is mainly localized in the cytoplasm of metamorphic molting larval midgut cells. The knockdown of Yki in the feeding larvae promotes larval-pupal transition, midgut programmed cell death, and repressed IAP1 (inhibitor of apoptosis 1) expression. Knockdown of Yki in the epidermal cell line (HaEpi) induced increased activation of Caspase3/7. Overexpressed Yki in HaEpi cells was mainly localized in the nucleus and induced cell proliferation. 20E promotes the cytoplasmic localization of Yki, reducing the expression of the IAP1, resulting in apoptosis. 20E promotes cytoplasmic retention of Yki by increasing Yki phosphorylation levels and promoting the interaction between Yki and the adaptor protein 14-3-3-ϵ. This regulation of Yki suppresses cell proliferation and induces cell apoptosis.

Juvenile hormone prevents 20-hydroxyecdysone-induced metamorphosis by regulating the phosphorylation of a newly identified broad protein.

The steroid hormone 20-hydroxyecdysone (20E) initiates insect molting and metamorphosis. By contrast, juvenile hormone (JH) prevents metamorphosis. However, the mechanism by which JH inhibits metamorphosis remains unclear. In this study, we propose that JH induces the phosphorylation of Broad isoform Z7 (BrZ7), a newly identified protein, to inhibit 20E-mediated metamorphosis in the lepidopteran insect Helicoverpa armigera. The knockdown of BrZ7 in larvae inhibited metamorphosis by repressing the expression of the 20E response gene. BrZ7 was weakly expressed and phosphorylated during larval growth but highly expressed and non-phosphorylated during metamorphosis. JH regulated the rapid phosphorylation of BrZ7 via a G-protein-coupled receptor-, phospholipase C-, and protein kinase C-triggered pathway. The phosphorylated BrZ7 bound to the 5'-regulatory region of calponin to regulate its expression in the JH pathway. Exogenous JH induced BrZ7 phosphorylation to prevent metamorphosis by suppressing 20E-related gene transcription. JH promoted non-phosphorylated calponin interacting with ultraspiracle protein to activate the JH pathway and antagonize the 20E pathway. This study reveals one of the possible mechanisms by which JH counteracts 20E-regulated metamorphosis by inducing the phosphorylation of BrZ7.

[A Method to Estimate Metal Abundance from Stellar Spectra Using Ca Line Index].

This paper presents a method to estimate stellar metallicity based on BP neural network and Ca line index. This method trains a BP ANN model from SDSS/SEGUE stellar spectra and parameters provided by SSPP. The values of Teff and the line index of Ca lines are the input of network while the [Fe/H] values are the oputput of the network. A set of samples are resampled from the set of all and then a network model is trained. The network can be used to predict the stellar metallicity from low-resolution spsectra. The experiment shows that the proposed method can accurately and effectively measure the [Fe/H] from the stellar spectra.

The hormone-dependent function of Hsp90 in the crosstalk between 20-hydroxyecdysone and juvenile hormone signaling pathways in insects is determined by differential phosphorylation and protein interactions.

BACKGROUND: Heat shock protein 90 (Hsp90) interacts with steroid hormone receptors, signaling kinases, and various transcription factors. However, the mechanism by which Hsp90 interacts with different proteins in various pathways remains unclear. METHODS: Western blot was used to study Hsp90 expression profile in Helicoverpa armigera (Lepidoptera). RNA interference was performed to investigate the function of Hsp90 in 20-hydroxyecdysone (20E) and juvenile hormone (JH) signal pathways. The binding of Hsp90 to the transcription factor ultraspiracle protein (USP1) and JH candidate receptor methoprene-tolerant (Met1) was analyzed by co-immunoprecipitation. Phospho-(Ser) PKC substrate antibody was used to detect Hsp90 phosphorylation. RESULTS: Hsp90 participated in 20E- or JH-induced gene expression. 20E induced the interaction between Hsp90 and USP1, whereas JH III and methoprene induced the interaction between Hsp90 and Met1, respectively. 20E and JH counteracted each other for these protein interactions. Both JH III and methoprene induced protein kinase C (PKC) phosphorylation of Hsp90. This process could be inhibited by phospholipase C (PLC) and PKC inhibitors. 20E suppressed JH III- or methoprene-induced PKC phosphorylation of Hsp90. CONCLUSION: 20E maintained the non-PKC-phosphorylation status of Hsp90. Hsp90 interacted with USP1 to induce gene expression in the 20E pathway. JH regulated the PKC-phosphorylation status of Hsp90. Hsp90 also interacted with Met1 to induce gene expression in the JH pathway. GENERAL SIGNIFICANCE: Our study describes a novel mechanism of Hsp90 action by altering phosphorylation and protein interaction in various hormonal signaling pathways.

[Atmospheric parameter estimation for LAMOST/GUOSHOUJING spectra].

It is a key task to estimate the atmospheric parameters from the observed stellar spectra in exploring the nature of stars and universe. With our Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) which begun its formal Sky Survey in September 2012, we are obtaining a mass of stellar spectra in an unprecedented speed. It has brought a new opportunity and a challenge for the research of galaxies. Due to the complexity of the observing system, the noise in the spectrum is relatively large. At the same time, the preprocessing procedures of spectrum are also not ideal, such as the wavelength calibration and the flow calibration. Therefore, there is a slight distortion of the spectrum. They result in the high difficulty of estimating the atmospheric parameters for the measured stellar spectra. It is one of the important issues to estimate the atmospheric parameters for the massive stellar spectra of LAMOST. The key of this study is how to eliminate noise and improve the accuracy and robustness of estimating the atmospheric parameters for the measured stellar spectra. We propose a regression model for estimating the atmospheric parameters of LAMOST stellar(SVM(lasso)). The basic idea of this model is: First, we use the Haar wavelet to filter spectrum, suppress the adverse effects of the spectral noise and retain the most discrimination information of spectrum. Secondly, We use the lasso algorithm for feature selection and extract the features of strongly correlating with the atmospheric parameters. Finally, the features are input to the support vector regression model for estimating the parameters. Because the model has better tolerance to the slight distortion and the noise of the spectrum, the accuracy of the measurement is improved. To evaluate the feasibility of the above scheme, we conduct experiments extensively on the 33 963 pilot surveys spectrums by LAMOST. The accuracy of three atmospheric parameters is log Teff: 0.006 8 dex, log g: 0.155 1 dex, [Fe/H]: 0.104 0 dex.

[A new method of sparse feature extraction for stellar spectra].

The authors propose a novel method of feature extraction for stellar spectra parameterization. The basic procedures are: First, stellar spectra are decomposed by multi-scale Harr wavelet and the coefficients with high-frequency are rejected. Secondly, the optimal features are detected by the lasso algorithm. Finally, we input the optimal feature vector to non-parametric regression model to estimate the atmospheric parameters. Haar wavelet can remove the high-frequency noise from the stellar spectrum. Lasso algorithm can further compress data by analyzing their significance on parameterization and removing redundancy. Experiments show that the proposed Haar+lasso method improves the accuracy and efficiency of the estimation. The authors used this scheme to estimate the atmospheric parameters from a subsample of some 40,000 stellar spectra from SDSS. The accuracies of our predictions (mean absolute errors) for each parameter are 0.0071 dex for log Teff, 0.2252 dex for log g, and 0.1996 dex for [Fe/H]. Compared with the results of the existing literature, this scheme can derive more accurate atmospheric parameters.

Nature of back slopping kombucha fermentation process: insights from the microbial succession, metabolites composition changes and their correlations.

Kombucha, a fermented tea prepared with a symbiotic culture of bacteria and yeast (SCOBY), offers a unique and unpredictable home-brewed fermentation process. Therefore, the need for a controlled kombucha fermentation process has become evident, which requiring a thorough understanding of the microbial composition and its relationship with the metabolites produced. In this study, we investigated the dynamics of microbial communities and metabolites over a 12-day fermentation period of a conventional kombucha-making process. Our findings revealed similarities between the microbial communities in the early (0-2 days) and late (10-12 days) fermentation periods, supporting the principle of back-slopping fermentation. Untargeted metabolite analysis unveiled the presence of harmful biogenic amines in the produced kombucha, with concentrations increasing progressively throughout fermentation, albeit showing relatively lower abundance on days 8 and 12. Additionally, a contrasting trend between ethanol and caffeine content was observed. Canonical correspondence analysis highlighted strong positive correlations between specific bacterial/yeast strains and identified metabolites. In conclusion, our study sheds light on the microbial and metabolite dynamics of kombucha fermentation, emphasizing the importance of microbial control and quality assurance measures in the production process.

[A novel scheme SVR(HAAR) for automatically estimating stellar atmospheric parameters from spectrum].

A novel scheme SVR(Haar) is proposed in the present work for automatically estimating the physical parameters of stellar spectra. The observed spectrum is disturbed usually by noise which is caused by the universe radiation, the atmosphere and observation equipment. Furthermore, the noise usually is the component of the spectrum with higher frequency. Therefore, we propose to extract features with Haar wavelet by removing higher frequency components. Researches show that this procedure can improve the accuracy of the estimation. Secondly, the support vector regression model is employed for estimating physical parameters of the stellar spectra. In this method, the epsilon insensitive domain techniques can further improve the probability to the slight distortion of the spectrum from imperfect calibration, and enhance the robustness of the proposed scheme. To check the effectiveness of the proposed scheme SVR(Haar), we did experiments extensively on authoritative simulated stellar spectra and real spectra observed by SLOAN, and compared it with the typical methods in the literature. The results show that the SVR (Haar) is better than the principal component analysis and non-parametric regression model in the literature.

[Efficient computation of spectral flux normalization].

Flux normalization is a key procedure in spectral data mining, and is important for the efficiency and accuracy of automatic processing of massive astronomical spectral data, information extraction and sharing. Since the usual implementation of flux normalizing methods is inefficient, the present work focuses on the algorithm designing of spectral flux normalization. Firstly, the authors investigated the limit efficiency characteristics of the available flux normalization methods, introduced four efficient flux normalizing algorithms, and studied their time complexity and space complexity. Secondly, the authors evaluated the efficiency of the proposed algorithms experimentally and horizontally based on the SDSS (Sloan Digital Sky Survey) released spectral data. In the theoretical research, the main consideration is the computational complexity characteristics of the flux normalization methods when the data size increases unlimitedly. The experimental research focuses on the difference in the computational burden between the basic operations in different flux normalization methods. It is shown that, although the four flux normalization methods S(max), S(median), S(mean) and S(unit) belong to the same limit efficiency type, on the spectra with usual observing scale, S(max) and S(median) are much more efficient than S(mean) and S(unit), and S(unit) is the most inefficient one. This work is helpful for choosing the appropriate flux normalization method based on the size of spectra database and the scientific needs in automatic spectra analysis.

[Automatic measurement of physical parameters of stellar spectra based on the Haar wavelet features].

The present paper researches the automatic measurement of the physical parameters ofthe stellar spectra. It is an important problem of the automatic processing of mass spectral data in the large-scale survey plan. The basic steps of the program in this article are: at first, the stellar spectra are decomposed by multi-scale Harr wavelet. Secondly, wavelet coefficients are chosen as the feature vectors of the spectrum. Finally, Non-parameter estimation is employed for estimating physical parameters of the stellar spectra. Studies show that the original spectrumonly needs to be decomposed by four-level Harr wavelet. If the wavelet coefficient at the fourth level is chosen as the wavelet feature of the spectrum, the surface gravity and effective temperature is estimated better. If the wavelet coefficient at the first level is chosen as the wavelet feature of the spectrum, the metallic abundance is estimated better. The authors use the spectral data in the literature ELODIE library to test the effectiveness of the method. When the wavelet coefficient is chosen as the feature vector of the spectrum, the experiment results show that the proposed method is robust and features high accuracy for the automatic measurement of the surface gravity, the effective temperature and the metallic abundance.

[A novel spectrum feature extraction method].

The present focuses on the celestial spectra feature extraction problem, which is a key procedure in automatic spectra classification. By extracting features, the authors can reduce redundancy, alleviate noise influence, and improve accuracy and efficiency in spectra classification. The authors introduced a novel feature analysis framework STP (space transformation and partition), which focuses on four essential components in feature extraction: decompose and reorganize spectrum components, reorganize, alleviate noise influence and eliminate redundancy. Based on STP, we can analyze most of the available feature extraction methods, for example, the unsupervised methods principal component analysis (PCA), wavelet transform, the supervised methods support vector machine (SVM), relevance vector machine (RVM), linear discriminant analysis (LDA), etc. We introduced a novel feature analysis framework and proposed a novel feature extraction method. The outstanding characteristics of the proposed method are its simplicity and efficiency. Researches show that it is sufficient to extract features by the proposed method in some cases, and it is not necessary to use the sophisticated methods, which is usually more complex in computation. The proposed method is evaluated in classifying Galaxy and QSO spectra, which is disturbed by red shift and is representative in automatic spectra classification research. The results are practical and helpful to gain novel insight into the traditional feature extraction methods and design more efficient spectrum classification method.

[Galaxy/quasar classification based on nearest neighbor method].

With the wide application of high-quality CCD in celestial spectrum imagery and the implementation of many large sky survey programs (e. g., Sloan Digital Sky Survey (SDSS), Two-degree-Field Galaxy Redshift Survey (2dF), Spectroscopic Survey Telescope (SST), Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) program and Large Synoptic Survey Telescope (LSST) program, etc.), celestial observational data are coming into the world like torrential rain. Therefore, to utilize them effectively and fully, research on automated processing methods for celestial data is imperative. In the present work, we investigated how to recognizing galaxies and quasars from spectra based on nearest neighbor method. Galaxies and quasars are extragalactic objects, they are far away from earth, and their spectra are usually contaminated by various noise. Therefore, it is a typical problem to recognize these two types of spectra in automatic spectra classification. Furthermore, the utilized method, nearest neighbor, is one of the most typical, classic, mature algorithms in pattern recognition and data mining, and often is used as a benchmark in developing novel algorithm. For applicability in practice, it is shown that the recognition ratio of nearest neighbor method (NN) is comparable to the best results reported in the literature based on more complicated methods, and the superiority of NN is that this method does not need to be trained, which is useful in incremental learning and parallel computation in mass spectral data processing. In conclusion, the results in this work are helpful for studying galaxies and quasars spectra classification.

[RVM supervised feature extraction and Seyfert spectra classification].

With recent technological advances in wide field survey astronomy and implementation of several large-scale astronomical survey proposals (e. g. SDSS, 2dF and LAMOST), celestial spectra are becoming very abundant and rich. Therefore, research on automated classification methods based on celestial spectra has been attracting more and more attention in recent years. Feature extraction is a fundamental problem in automated spectral classification, which not only influences the difficulty and complexity of the problem, but also determines the performance of the designed classifying system. The available methods of feature extraction for spectra classification are usually unsupervised, e. g. principal components analysis (PCA), wavelet transform (WT), artificial neural networks (ANN) and Rough Set theory. These methods extract features not by their capability to classify spectra, but by some kind of power to approximate the original celestial spectra. Therefore, the extracted features by these methods usually are not the best ones for classification. In the present work, the authors pointed out the necessary to investigate supervised feature extraction by analyzing the characteristics of the spectra classification research in available literature and the limitations of unsupervised feature extracting methods. And the authors also studied supervised feature extracting based on relevance vector machine (RVM) and its application in Seyfert spectra classification. RVM is a recently introduced method based on Bayesian methodology, automatic relevance determination (ARD), regularization technique and hierarchical priors structure. By this method, the authors can easily fuse the information in training data, the authors' prior knowledge and belief in the problem, etc. And RVM could effectively extract the features and reduce the data based on classifying capability. Extensive experiments show its superior performance in dimensional reduction and feature extraction for Seyfert classification.

[Qianlie Beixi capsule for unliquefiable semen: a clinical study of 180 cases].

OBJECTIVE: To investigate the efficacy and possible action mechanism of Qianlie Beixi Capsule in the treatment of unliquefiable semen. METHODS: A total of 240 patients with unliquefiable semen were randomly divided into a treatment group (n = 180), treated with Qianlie Beixi Capsule, and a control group (n = 60), given compound tablets of zinc and protein. The treatment lasted two courses of 45 days each, and the seminal changes were observed and recorded. RESULTS: Of the patients in the treatment group, 144 were cured, 12 responded and 24 failed to respond to the medication after the first course; and 158 were cured, 7 responded and 15 failed to after the second course, with the effectiveness rate of 91.67%. Meanwhile, sperm motility was obviously improved, with statistically significant difference from that of the controls (P < 0.01). CONCLUSION: Qianlie Beixi Capsule is effective for unliquefiable semen by improving the function of the prostate and shortening the time of seminal liquefaction.

Protein tyrosine kinase, JNK, and ERK involvement in pseudolaric acid B-induced apoptosis of human breast cancer MCF-7 cells.

AIM: To investigate the apoptotic mechanism of pseudolaric acid B (PAB) in human breast cancer MCF-7 cells. METHODS: 3-(4,5-Dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide analysis and morphological changes were applied to detect apoptosis. The percentage of apoptotic and necrotic cells were calculated by the lactate dehydrogenase activity-based cytotoxicity assay, and the protein expression was examined by Western blot analysis. RESULTS: PAB and/or the mitogen-activated protein kinases, including p38, c-Jun-N-terminal kinase (JNK) and extracellular signal-regulated kinase (ERK), did not participate in necrosis. P38 had no obvious function on apoptosis after 4 micromol/L PAB treatment for 36 h, but PAB induced JNK phosphorylation and inhibited ERK phosphorylation in the apoptotic process. In this study the inhibitor of protein tyrosine kinase (PTK) genistein inverted the inhibitory effect of PAB, instead promoting the survival of MCF-7 cells. Like genistein, another PTK inhibitor AG1024 had a similar effect on PAB-treated MCF-7 cells, indicating that PAB activated PTK to induce apoptosis. Together with PAB, genistein increased the expression of p-ERK, and decreased the expressions of JNK and p-JNK in PAB-treated MCF-7 cells at 36 h. And it is considered that the p-ERK and p-JNK were active patterns of ERK and JNK, respectively. CONCLUSION: PTK were upstream of ERK and JNK, and PTK induced apoptosis through activating JNK and inactivating ERK in PAB-treated MCF-7 cells.

[Supervised feature extraction based on FDA and galaxy spectra classification].

With the recent technological advances in wide field survey astronomy and the implementation of several large scale astronomical survey proposals, celestial spectra are becoming very rich and the study of automated processing methods is attracting more and more attention. In the present work, the authors pointed out that it is necessary to investigate supervised feature extraction by analyzing the characteristics of the spectra classification research in literature and the limitations of unsupervised feature extraction methods. And the authors studied supervised feature extraction based on Fisher discriminant analysis (FDA) and its application in galaxy spectra classification. FDA could effectively reduce dimension and extract the features based on the classifying capability by fusing information in training data. Experiments show its superior performance in dimensional reduction for galaxy spectra classification.

[Celestial spectrum flux standardization for classification].

Celestial spectra should be preprocessed before automated classification to eliminate the disturbance of noise, observa-tion environment, and flux aberrance. In the present work, the authors studied the spectrum flux standardization problem. By analyzing the disturbing factors and their characteristics, the authors put forward a theoretical model for spectra flux, and corre-spondingly give several flux standardizing methods. The rationality/correctness of the model, and the satisfactory performance of the proposed methods have been obtained by the experiments over normal galaxies (NGs) and quasi-stellar object (Qso). Furthermore, the authors theoretically analyze, compare and evaluate them. In particular, this work indicated that the conventional method is worse than the proposed one. And the investigation is also particularly significant for other automatic spectrum processing study, e. g. redshift determination, effective temperature, metallic estimation, etc.

[A method for obtaining redshifts of quasars based on wavelet multi-scaling feature matching].

The LAMOST project, the world's largest sky survey project being implemented in China, is expected to obtain 10(5) quasar spectra. The main objective of the present article is to explore methods that can be used to estimate the redshifts of quasar spectra from LAMOST. Firstly, the features of the broad emission lines are extracted from the quasar spectra to overcome the disadvantage of low signal-to-noise ratio. Then the redshifts of quasar spectra can be estimated by using the multi-scaling feature matching. The experiment with the 15, 715 quasars from the SDSS DR2 shows that the correct rate of redshift estimated by the method is 95.13% within an error range of 0. 02. This method was designed to obtain the redshifts of quasar spectra with relative flux and a low signal-to-noise ratio, which is applicable to the LAMOST data and helps to study quasars and the large-scale structure of the universe etc.

The steroid hormone 20-hydroxyecdysone promotes switching from autophagy to apoptosis by increasing intracellular calcium levels.

Autophagy regulates cell survival (or cell death in several cases), whereas apoptosis regulates cell death. However, the relationship between autophagy and apoptosis and the regulative mechanism is unclear. We report that steroid hormone 20-hydroxyecdysone (20E) promotes switching from autophagy to apoptosis by increasing intracellular calcium levels in the midgut of the lepidopteran insect Helicoverpa armigera. Autophagy and apoptosis sequentially occurred during midgut programmed cell death under 20E regulation, in which lower concentrations of 20E induced microtubule-associated protein 1 light chain 3-phosphatidylethanolamine (LC3-II, also known as autophagy-related gene 8, ATG8) expression and autophagy. High concentrations of 20E induced cleavage of ATG5 to NtATG5 and pro-caspase-3 to active caspase-3, which led to a switch from autophagy to apoptosis. Blocking autophagy by knockdown of ATG5, ATG7, or ATG12, or with the autophagy inhibitor 3-methyladenine, inhibited 20E-induced autophagy and apoptosis. Blocking apoptosis by using the apoptosis inhibitor Ac-DEVD-CHO did not prevent 20E-induced autophagy, suggesting that apoptosis relies on autophagy. ATG5 knockdown resulted in abnormal pupation and delayed pupation time. High concentrations of 20E induced high levels of intracellular Ca2+, NtATG5, and active caspase-3, which mediated the switch from autophagy to apoptosis. Blocking 20E-mediated increase of cellular Ca2+ caused a decrease of NtATG5 and active caspase-3 and repressed the transformation from autophagy to apoptosis, thereby promoting cell survival. 20E induces an increase in the concentration of intracellular Ca2+, thereby switching autophagic cell survival to apoptotic cell death.

[A novel spectral classifier based on coherence measure].

Classification and discovery of new types of celestial bodies from voluminous celestial spectra are two important issues in astronomy, and these two issues are treated separately in the literature to our knowledge. In the present paper, a novel coherence measure is introduced which can effectively measure the coherence of a new spectrum of unknown type with the training sampleslocated within its neighbourhood, then a novel classifier is designed based on this coherence measure. The proposed classifier is capable of carrying out spectral classification and knowledge discovery simultaneously. In particular, it can effectively deal with the situation where different types of training spectra exist within the neighbourhood of a new spectrum, and the traditional k-nearest neighbour method usually fails to reach a correct classification. The satisfactory performance for classification and knowledge discovery has been obtained by the proposed novel classifier over active galactic nucleus (AGNs) and active galaxies (AGs) data.