The accuracy of extrapolation methods; an automatic box–jenkins package sift

Hill and Woodworth (1980) proposed an algorithm suitable for identifying Box–Jenkins models automatically without reliance on the investigator. This paper first reviews the method. It is then used on the 111 series analysed by Anderson in the Makridakis forecasting competition. The results show that the automatic method of Hill and Woodworth is comparable in terms of accuracy to the full Box–Jenkins identification procedure.     

Pharmacological versus binding analysis of receptor systems: how do they interplay? Myometrial cell receptors for oxytocin as a paradigm.

Binding studies in various biological systems frequently indicate the presence of several binding sites for a biologically active ligand. They differ in their affinity for the ligand in question, binding capacity, and Hill coefficient, which suggests differences in the mechanisms of the binding site-ligand interactions. Identification of the 'true' receptors (sites initiating a cellular response) appears to be difficult. Three clusters of binding sites for oxytocin were found on rat myometrial cells. The oxytocin receptor seems to be linked to the medium-affinity site; the cooperation between the high- and medium-affinity sites in eliciting the uterotonic response seems likely, but lacks experimental proof. Dose-response analysis in partially irreversibly inhibited uterus preparations, the method of equipotent doses (Furchgott-Bursztyn method), and structure-activity analysis of oxytocin-like peptides acting as competitive inhibitors of oxytocin, turned out to be suitable for pharmacological analysis of this receptor system.     

Evidence for an olfactory receptor which responds to nicotine--nicotine as an odorant

The tobacco alkaloid (S)(-)-nicotine, when applied as a vapour to an in vitro head preparation, stimulates the olfactory epithelium in three strains of rats and to a lesser extent in two strains of mice. The electro-olfactogram (EOG) generated by nicotine has similar characteristics to the EOGs produced by known odorants. The nicotine EOG increases with increasing concentration of nicotine vapour (1-100 nM) applied to the olfactory epithelium. Differential reduction of the nicotine EOG by the lectin concanavalin A is seen in Wistar and Lister Hooded rats. The reduction of the nicotine EOG by concanavalin A is prevented by adding alpha-methyl-D-mannoside to the lectin superfusion medium. This suggests that there is a glyco-moiety associated with at least one olfactory receptor responding to nicotine. Our results suggest that rat olfactory epithelium has receptor sites for nicotine. Nicotine is an unusual compound because it shows both odorant and pharmacological properties.     

Evidence for an olfactory receptor which responds to nicotine — nicotine as an odorant

Summary The tobacco alkaloid (S)(–)-nicotine, when applied as a vapour to an in vitro head preparation, stimulates the olfactory epithelium in three strains of rats and to a lesser extent in two strains of mice. The electro-olfactogram (EOG) generated by nicotine has similar characteristics to the EOGs produced by known odorants. The nicotine EOG increases with increasing concentration of nicotine vapour (1–100 nM) applied to the olfactory epithelium.Differential reduction of the nicotine EOG by the lectin concanavalin A is seen in Wistar and Lister Hooded rats. The reduction of the nicotine EOG by concanavalin A is prevented by adding alpha-methyl-D-mannoside to the lectin superfusion medium. This suggests that there is a glyco-moiety associated with at least one olfactory receptor responding to nicotine.Our results suggest that rat olfactory epithelium has receptor sites for nicotine. Nicotine is an unusual compound because it shows both odorant and pharmacological properties.22 September 1986     

Heat balance modelling

The only way to describe the effects of the thermal environment on the human body completely is to do it by means of an energy balance equation. In such an equation all relevant meteorological parameters, behavioral characteristics (activity and clothing) and body measurements can be considered. Using Fanger's comfort equation and the models MEMI and IMEM as examples, the problems of energy balancing and ways of solving them are described. The value of energy balance models is documented by examples from the field of application.     

Pharmacological versus binding analysis of receptor systems: How do they interplay? Myometrial cell receptors for oxytocin as a paradigm

Summary Binding studies in various biological systems frequently indicate the presence of several binding sites for a biologically active ligand. They differ in their affinity for the ligand in question, binding capacity, and Hill coefficient, which suggests differences in the mechanisms of the binding site-ligand interactions. Identification of the true receptors (sites initiating a cellular response) appears to be difficult. Three clusters of binding sites for oxytocin were found on rat myometrial cells. The oxytocin receptor seems to be linked to the medium-affinity site; the cooperation between the high-and medium-affinity sites in eliciting the uterotonic response seems likely, but lacks experimental proof. Dose-response analysis in partially irreversibly inhibited uterus preparations, the method of equipotent doses (Furchgott-Bursztyn method), and structure-activity analysis of oxytocin-like peptides acting as competitive inhibitors of oxytocin, turned out to be suitable for pharmacological analysis of this receptor system.     

Molecular basis of parathyroid hormone receptor signaling and trafficking: a family B GPCR paradigm

The parathyroid hormone (PTH) receptor type 1 (PTHR), a G protein-coupled receptor (GPCR), transmits signals to two hormone systems—PTH, endocrine and homeostatic, and PTH-related peptide (PTHrP), paracrine—to regulate different biological processes. PTHR responds to these hormonal stimuli by activating heterotrimeric G proteins, such as G_S that stimulates cAMP production. It was thought that the PTHR, as for all other GPCRs, is only active and signals through G proteins on the cell membrane, and internalizes into a cell to be desensitized and eventually degraded or recycled. Recent studies with cultured cell and animal models reveal a new pathway that involves sustained cAMP signaling from intracellular domains. Not only do these studies challenge the paradigm that cAMP production triggered by activated GPCRs originates exclusively at the cell membrane but they also advance a comprehensive model to account for the functional differences between PTH and PTHrP acting through the same receptor.     

Semi‐Structural Forecasting of UK Inflation Based on the Hybrid New Keynesian Phillips Curve

We develop a semi‐structural model for forecasting inflation in the UK in which the New Keynesian Phillips curve (NKPC) is augmented with a time series model for marginal cost. By combining structural and time series elements we hope to reap the benefits of both approaches, namely the relatively better forecasting performance of time series models in the short run and a theory‐consistent economic interpretation of the forecast coming from the structural model. In our model we consider the hybrid version of the NKPC and use an open‐economy measure of marginal cost. The results suggest that our semi‐structural model performs better than a random‐walk forecast and most of the competing models (conventional time series models and strictly structural models) only in the short run (one quarter ahead) but it is outperformed by some of the competing models at medium and long forecast horizons (four and eight quarters ahead). In addition, the open‐economy specification of our semi‐structural model delivers more accurate forecasts than its closed‐economy alternative at all horizons. Copyright © 2014 John Wiley & Sons, Ltd.     

The neurotrophic receptor TrkB: a drug target in anti-cancer therapy?

Increasing evidence implies altered signaling through the neurotrophic receptor tyrosine kinase TrkB in promoting tumor formation and metastasis. TrkB, sometimes in conjunction with its primary ligand BDNF, is often overexpressed in a variety of human cancers, ranging from neuroblastomas to pancreatic ductal adenocarcinomas, in which it may allow tumor expansion and contribute to resistance to anti-tumor agents. In vitro, TrkB acts as a potent suppressor of anoikis (detachment-induced apoptosis), which is associated with the acquisition of an aggressive tumorigenic and metastatic phenotype in vivo. In view of its predicted contribution to tumorigenicity and metastasis in humans, TrkB corresponds to a potential drug target, and preclinical models have already been established. The encouraging results of pharmacological Trk inhibitors in tumor xenograft models suggest that TrkB inhibition may represent a promising novel anti-tumor therapeutic strategy. This hypothesis is currently being evaluated in clinical trials. Here, we will discuss the latest developments on TrkB in these contexts as well as highlight some critical questions that remain to be addressed for evaluating TrkB as a therapeutic target in cancer. Received 12 October 2005; received after revision 19 December 2005; accepted 11 January 2006     

Modeling multivariate co integrated systems: Insights from non-linear dynamics

Johansen's test for co integration is applied to Litterman's original six-variable Bayesian vector auto regression (BVAR) model to obtain vector error correction mechanism (VECM) and Bayesian error correction (BECM) versions of the model. The Brock, Dechert, and Scheinkman (BDS) test for independence from the non-linear dynamics literature is then applied to the error structures of each estimated equation of the BECM and VECM models, plus two BVAR versions of the model. The results show that none of the models produce independent and identically distributed (IID) errors for all six equations. However, the BDS results suggest the elimination of the Bayesian prior from the BECM model, given that the univariate VECM errors are IID in five equations, compared to only two or three equations under the three Bayesian restricted models. These results combined with previous evidence regarding the superior forecasting performance of BECM over ECM models suggest future experimentation with less restrictive BVAR priors, BECM models corrected for heteroscedasticity, or hybrid specifications based on the nonlinear dynamics literature.     

On the forecasting of high-frequency financial time series based on ARIMA model improved by deep learning

Through empirical research, it is found that the traditional autoregressive integrated moving average (ARIMA) model has a large deviation for the forecasting of high-frequency financial time series. With the improvement in storage capacity and computing power of high-frequency financial time series, this paper combines the traditional ARIMA model with the deep learning model to forecast high-frequency financial time series. It not only preserves the theoretical basis of the traditional model and characterizes the linear relationship, but also can characterize the nonlinear relationship of the error term according to the deep learning model. The empirical study of Monte Carlo numerical simulation and CSI 300 index in China show that, compared with ARIMA, support vector machine (SVM), long short-term memory (LSTM) and ARIMA-SVM models, the improved ARIMA model based on LSTM not only improves the forecasting accuracy of the single ARIMA model in both fitting and forecasting, but also reduces the computational complexity of only a single deep learning model. The improved ARIMA model based on deep learning not only enriches the models for the forecasting of time series, but also provides effective tools for high-frequency strategy design to reduce the investment risks of stock index.     

The Quantification and Differentiation of the Drug Receptor Theory,c. 1910–1960

While historians have dealt with the origins of the concept of drug receptors in the work of Paul Ehrlich (1854–1915) and John N. Langley (1852–1925) as well as with some of its applications in modern pharmaceutical research, the history of the receptor theory as such has been neglected. Discussing major developments and conceptual changes in receptor theory between about 1910 and 1960 (including relevant contributions by A. V. Hill, A. J. Clark, J. H. Gaddum, E. J. Ariëns and others), this paper attempts to fill this gap in historiography. It provides a case study of the unfolding of research under a new paradigm, but it considers also contemporary criticism and scepticism. By the early 1960s, quantitative investigations of drug action and interpretations of the experimental findings in terms of the receptor concept had become constitutive of the emerging field of ‘molecular pharmacology’. Even then, however, receptors were still hypothetical entities.     

The quantification and differentiation of the drug receptor theory, c. 1910-1960

While historians have dealt with the origins of the concept of drug receptors in the work of Paul Ehrlich (1854-1915) and John N. Langley (1852-1925) as well as with some of its applications in modern pharmaceutical research, the history of the receptor theory as such has been neglected. Discussing major developments and conceptual changes in receptor theory between about 1910 and 1960 (including relevant contributions by A. V. Hill, A. J. Clark, J. H. Gaddum, E. J. Ari?ns and others), this paper attempts to fill this gap in historiography. It provides a case study of the unfolding of research under a new paradigm, but it considers also contemporary criticism and scepticism. By the early 1960s, quantitative investigations of drug action and interpretations of the experimental findings in terms of the receptor concept had become constitutive of the emerging field of 'molecular pharmacology'. Even then, however, receptors were still hypothetical entities.     

Trefoil factors

Trefoil Factor 1 (TFF1), the first member of the trefoil factor family, is normally expressed in the stomach mucosa. Ectopic expression is also observed in various human pathological conditions, notably in numerous carcinomas and gastrointestinal acute inflammatory disorders. In vivo experimental data using TFF1-deficient mice highlight the pleiotropic functions of TFF1: (i) it is a gastric tumor suppressor gene involved in gastric ontogenesis and homeostasis; (ii) it protects gut mucosa from aggression; (iii) it participates in folding secreted proteins inside the endoplasmic reticulum. At the cellular level, it limits cell proliferation and apoptosis, and favors cell differentiation. Collectively, these data suggest that TFF1 may provide an alternative pharmacological tool for the prevention and treatment of human gastrointestinal diseases.     

Assessing the historical accuracy of regional economic forecasts

This study addresses for the first time systematic evaluation of a widely used class of forecasts, regional economic forecasts. Ex ante regional structural equation model forecasts are analysed for 19 metropolitan areas. One- to ten-quarter-ahead forecasts are considered and the seven-year sample spans a complete business cycle. Counter to previous speculation in the literature, (1) dependency on macroeconomic forecasting model inputs does not substantially erode accuracy relative to univariate extrapolative methodologies and (2) stochastic time series models do not on average, yield more accurate regional economic predictions than structural models. Similar to findings in other studies, clear preferences among extrapolative methodologies do not emerge. Most general conclusions, however, are subject to caveats based on step-length effects and region-specific effects.     

A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back-propagation neural network for mid-short-term load forecasting

Since load forecasting plays a decisive role in the safe and stable operation of power systems, it is particularly important to explore forecasting methods accurately. In this article, the hybrid empirical mode decomposition (EMD) and support vector regression (SVR) with back-propagation neural network (BPNN), namely the EMDHR-SVR-BPNN model, is proposed. Information theory is mainly used to solve the data tendency problem, and the EMD method is used to solve the data volatility problem. There is no interaction between these two methods; thus these two models can complement each other through generalized regression of orthogonal decomposition. Taking the load data from the New South Wales (NSW, Australia) market as an example, the obtained simulation results are compared with other models. It is concluded that the proposed EMDHR-SVR-BPNN model not only improves the forecasting accuracy but also has good fitting ability. It can reflect the changing tendency of data in a timely manner, providing a strong basis for the electricity generation of the power sector in the future, thus reducing electricity waste. The proposed EMDHR-SVR-BPNN model has potential for employment in mid-short term load forecasting.     

Toxin bioportides: exploring toxin biological activity and multifunctionality

Toxins have been shown to have many biological functions and to constitute a rich source of drugs and biotechnological tools. We focus on toxins that not only have a specific activity, but also contain residues responsible for transmembrane penetration, which can be considered bioportides—a class of cell-penetrating peptides that are also intrinsically bioactive. Bioportides are potential tools in pharmacology and biotechnology as they help deliver substances and nanoparticles to intracellular targets. Bioportides characterized so far are peptides derived from human proteins, such as cytochrome c (CYCS), calcitonin receptor (camptide), and endothelial nitric oxide synthase (nosangiotide). However, toxins are usually disregarded as potential bioportides. In this review, we discuss the inclusion of some toxins and molecules derived thereof as a new class of bioportides based on structure activity relationship, minimization, and biological activity studies. The comparative analysis of the amino acid residue composition of toxin-derived bioportides and their short molecular variants is an innovative analytical strategy which allows us to understand natural toxin multifunctionality in vivo and plan novel pharmacological and biotechnological products. Furthermore, we discuss how many bioportide toxins have a rigid structure with amphiphilic properties important for both cell penetration and bioactivity.     

Forecasting with an econometric model: The ‘ragged edge’ problem

In practical econometric forecasting exercises, incomplete data on current and immediate past values of endogenous variables are available. This paper considers various approaches to this ‘ragged edge’ problem, including the common device of treating as ‘temporarily exogenous’ an endogenous variable whose value is known, by deleting it from the set of endogenous variables for whose forecast values the model is solved and suppressing the corresponding structural equation. It is seen that this forecast can be adjusted to coincide with the optimal forecast. The initial discussion concerns the textbook linear simultaneous equation model; extensions to non-linear dynamic models are described.     

Cilioplasm is a cellular compartment for calcium signaling in response to mechanical and chemical stimuli

Primary cilia with a diameter of ~200 nm have been implicated in development and disease. Calcium signaling within a primary cilium has never been directly visualized and has therefore remained a speculation. Fluid-shear stress and dopamine receptor type-5 (DR5) agonist are among the few stimuli that require cilia for intracellular calcium signal transduction. However, it is not known if these stimuli initiate calcium signaling within the cilium or if the calcium signal originates in the cytoplasm. Using an integrated single-cell imaging technique, we demonstrate for the first time that calcium signaling triggered by fluid-shear stress initiates in the primary cilium and can be distinguished from the subsequent cytosolic calcium response through the ryanodine receptor. Importantly, this flow-induced calcium signaling depends on the ciliary polycystin-2 calcium channel. While DR5-specific agonist induces calcium signaling mainly in the cilioplasm via ciliary CaV1.2, thrombin specifically induces cytosolic calcium signaling through the IP₃ receptor. Furthermore, a non-specific calcium ionophore triggers both ciliary and cytosolic calcium responses. We suggest that cilia not only act as sensory organelles but also function as calcium signaling compartments. Cilium-dependent signaling can spread to the cytoplasm or be contained within the cilioplasm. Our study thus provides the first model to understand signaling within the cilioplasm of a living cell.     

Hybridizing kernel‐based fuzzy c‐means with hierarchical selective neural network ensemble model for business failure prediction

More and more ensemble models are used to forecast business failure. It is generally known that the performance of an ensemble relies heavily on the diversity between each base classifier. To achieve diversity, this study uses kernel‐based fuzzy c‐means (KFCM) to organize firm samples and designs a hierarchical selective ensemble model for business failure prediction (BFP). First, three KFCM methods—Gaussian KFCM (GFCM), polynomial KFCM (PFCM), and Hyper‐tangent KFCM (HFCM)—are employed to partition the financial data set into three data sets. A neural network (NN) is then adopted as a basis classifier for BFP, and three sets, which are derived from three KFCM methods, are used to build three classifier pools. Next, classifiers are fused by the two‐layer hierarchical selective ensemble method. In the first layer, classifiers are ranked based on their prediction accuracy. The stepwise forward selection method is employed to selectively integrate classifiers according to their accuracy. In the second layer, three selective ensembles in the first layer are integrated again to acquire the final verdict. This study employs financial data from Chinese listed companies to conduct empirical research, and makes a comparative analysis with other ensemble models and all its component models. It is the conclusion that the two‐layer hierarchical selective ensemble is good at forecasting business failure.