Unknown Structural Changes Research Articles

Delta SARS-CoV-2 s2m Structure, Dynamics, and Entropy: Consequences of the G15U Mutation.

Bioinformatic analysis of the Delta SARS-CoV-2 genome reveals a single nucleotide mutation (G15U) in the stem-loop II motif (s2m) relative to ancestral SARS-CoV-2. Despite sequence similarity, unexpected differences between SARS-CoV-2 and Delta SARS-CoV-2 s2m homodimerization experiments require the discovery of unknown structural and thermodynamic changes necessary to rationalize the data. Using our reported SARS-CoV-2 s2m model, we induced the G15U substitution and performed 3.5 microseconds of unbiased molecular dynamics simulation at 283 and 310 K. The resultant Delta s2m adopted a secondary structure consistent with our reported NMR data, resulting in significant deviations in the tertiary structure and dynamics from our SARS-CoV-2 s2m model. First, we find differences in the overall three-dimensional structure, where the characteristic 90° L-shaped kink of the SARS-CoV-2 s2m did not form in the Delta s2m resulting in a "linear" hairpin with limited bending dynamics. Delta s2m helical parameters are calculated to align closely with A-form RNA, effectively eliminating a hinge point to form the L-shape kink by correcting an upper stem defect in SARS-CoV-2 induced by a noncanonical and dynamic G:A base pair. Ultimately, the shape difference rationalizes the migration differences in reported electrophoresis experiments. Second, increased fluctuation of the Delta s2m palindromic sequence, within the terminal loop, compared to SARS-CoV-2 s2m results in an estimated increase of entropy of 6.8 kcal/mol at 310 K relative to the SARS-CoV-2 s2m. The entropic difference offers a unique perspective on why the Delta s2m homodimerizes less spontaneously, forming fewer kissing dimers and extended duplexes compared to SARS-CoV-2. In this work, both the L-shape reduction and palindromic entropic penalty provides an explanation of our reported in vitro electrophoresis homodimerization results. Ultimately, the structural, dynamical, and entropic differences between the SARS-CoV-2 s2m and Delta s2m serve to establish a foundation for future studies of the s2m function in the viral lifecycle.

High-temperature pyrolysis behavior and structural evolution mechanism of graphene oxide: A ReaxFF molecular dynamics simulation

An in-depth understanding of the structural evolution of graphene oxide (GO) at high temperatures is of crucial importance for tuning the properties of GO and synthesizing graphene-related materials from GO, which still has many unknown structural changes at the atomic scale. In this work, the overall structural change process and the local structural evolution mechanism during the high-temperature pyrolysis of GO were investigated by the ReaxFF molecular dynamics simulation method. The detonation process of GO was explored for the first time at the atomic simulation level, and the detonation point of GO was found to be positively correlated with the C/O ratio, which is highly consistent with previous experimental results. The bonding pattern between C-O in GO was sorted out, and the desorption mechanism of functional groups during pyrolysis was explained in detail by analyzing the ratio of sp2 and sp3 hybridized C atoms. In addition, the effects of temperature, C/O ratio and functional group type on the small molecule pyrolysis products were also investigated. The present study provides strategies to tune GO structure by pyrolysis at high temperature.

Multi-Threshold Structural Equation Model

In this article, we consider the instrumental variable estimation for causal regression parameters with multiple unknown structural changes across subpopulations. We propose a multiple change point detection method to determine the number of thresholds and estimate the threshold locations in the two-stage least square procedure. After identifying the estimated threshold locations, we use the Wald method to estimate the parameters of interest, that is, the regression coefficients of the endogenous variable. Based on some technical assumptions, we carefully establish the consistency of estimated parameters and the asymptotic normality of causal coefficients. Simulation studies are included to examine the performance of the proposed method. Finally, our method is illustrated via an application of the Philippine farm households data for which some new findings are discovered.

Relevant parameter changes in structural break models

Structural break time series models, which are commonly used in macroeconomics and finance, capture unknown structural changes by allowing for abrupt changes to model parameters. However, many specifications suffer from an over-parametrization issue, since typically all parameters have to change when a break occurs. We introduce a sparse change-point model to detect which parameters change over time. We propose a shrinkage prior distribution, which controls model parsimony by limiting the number of parameters that change from one structural break to another. We develop a Bayesian sampler for inference on the sparse change-point model. An extensive simulation study based on AR, ARMA and GARCH processes highlights the excellent performance of the sampler. We provide several empirical applications including an out-of-sample forecasting exercise showing that the Sparse change-point framework compares favorably with other recent time-varying parameter processes.

Structural changes in heterogeneous panels with endogenous regressors

SummaryThis paper extends Pesaran's (Econometrica, 2006, 74, 967–1012) common correlated effects (CCE) by allowing for endogenous regressors in large heterogeneous panels with unknown common structural changes in slopes and error factor structure. Since endogenous regressors and structural breaks are often encountered in empirical studies with large panels, this extension makes Pesaran's CCE approach empirically more appealing. In addition to allowing for slope heterogeneity and cross‐sectional dependence, we find that Pesaran's CCE approach is also valid when dealing with unobservable factors in the presence of endogenous regressors and structural changes in slopes and error factor loadings. This is supported by Monte Carlo experiments.

Sparse Change-point HAR Models for Realized Variance

ABSTRACTChange-point time series specifications constitute flexible models that capture unknown structural changes by allowing for switches in the model parameters. Nevertheless most models suffer from an over-parametrization issue since typically only one latent state variable drives the switches in all parameters. This implies that all parameters have to change when a break happens. To gauge whether and where there are structural breaks in realized variance, we introduce the sparse change-point HAR model. The approach controls for model parsimony by limiting the number of parameters which evolve from one regime to another. Sparsity is achieved thanks to employing a nonstandard shrinkage prior distribution. We derive a Gibbs sampler for inferring the parameters of this process. Simulation studies illustrate the excellent performance of the sampler. Relying on this new framework, we study the stability of the HAR model using realized variance series of several major international indices between January 2000 and August 2015.

FINANCIAL DEVELOPMENT AND ECONOMIC GROWTH: SOME CARIBBEAN EMPIRICAL EVIDENCE

The paper examines the role of financial development and economic growth in Barbados, Jamaica and Trinidad and Tobago. Causality tests conducted by using the stepwise Granger causality method, after addressing respective unknown exogenous structural changes, and using bounds testing approach to determine the level relationships between economic growth and each respective real financial development proxies, produced more robust results. Thus, economic growth drives real financial development in the short-run in all three countries, with Trinidad-Tobagoi¯s results being overwhelming. Long-run weak exogeneity tests from respective factor loadings indicate similar demand-following phenomenon in Jamaica, although results are mixed in Barbados and Trinidad-Tobago. Policymakers are therefore advised to make the overall economic growth of Jamaica their policy priority, and not favor its financial market with special policies over both near term and long-run. Similar policy is strongly recommended for both Trinidad-Tobago and Barbados. However, in Barbados and Trinidad-Tobago, mixed Granger causal relationship results suggest that extending resources as incentives to boost up both financial market development and economic growth will benefit them over the long-run.

Genetic analysis of a rare case with Kallman syndrome and steroid sulfatase deficiency

To explore the pathogenesis of a patient featuring azoospermia and steroid sulfatase deficiency. Polymerase chain reaction (PCR), G-banded karyotyping and Illumina Human CytoSNP-12 Beadchip analysis were conducted. STS sites PCR showed that there was no deletion in the AZF zone. G-banding analysis indicated an unknown structural change in chromosome X, which was verified by single nucleotide polymorphism array (SNP array) as a 5.4 Mb deletion in Xp22.31-p22.33. The Xp22.31-p22.33 deletion probably underlies the Kallman syndrome and steroid sulfatase defect in the patient.

Sparse Change-Point Har Models for Realized Variance

Change-point time series specifications constitute flexible models that capture unknown structural changes by allowing for switches in the model parameters. Nevertheless most models suffer from an over-parametrization issue since typically only one latent state variable drives the switches in all parameters. This implies that all parameters have to change when a break happens. To gauge whether and where there are structural breaks in realized variance, we introduce the sparse change-point HAR model. The approach controls for model parsimony by limiting the number of parameters which evolve from one regime to another. Sparsity is achieved thanks to employing a nonstandard shrinkage prior distribution. We derive a Gibbs sampler for inferring the parameters of this process. Simulation studies illustrate the excellent performance of the sampler. Relying on this new framework, we study the stability of the HAR model using realized variance series of several major international indices between January 2000 and August 2015.

How Actin Initiates the Motor Activity of Myosin

Fundamental to cellular processes are directional movements driven by molecular motors. A common theme for these and other molecular machines driven by ATP is that controlled release of hydrolysis products is essential for using the chemical energy efficiently. Mechanochemical transduction by myosin motors on actin is coupled to unknown structural changes that result in the sequential release of inorganic phosphate (Pi) and MgADP. We present here a myosin structure possessing an actin-binding interface and a tunnel (back door) that creates an escape route for Pi with a minimal rotation of the myosin lever arm that drives movements. We propose that this state represents the beginning of the powerstroke on actin and that Pi translocation from the nucleotide pocket triggered by actin binding initiates myosin force generation. This elucidates how actin initiates force generation and movement and may represent a strategy common to many molecular machines.

Nonlinear multivariate time–space threshold vector error correction model for short term traffic state prediction

We propose Time–Space Threshold Vector Error Correction (TS-TVEC) model for short term (hourly) traffic state prediction. The theory and method of cointegration with error correction mechanism is employed in the general design of the new statistical model TS-TVEC. An inherent connection between mathematical form of error correction model and traffic flow theory is revealed through the transformation of the well-known Fundamental Traffic Diagrams. A threshold regime switching framework is implemented to overcome any unknown structural changes in traffic time series. Spatial cross correlated information is incorporated with a piecewise linear vector error correction model. A Neural Network model is also constructed in parallel to comparatively test the effectiveness and robustness of the new statistical model. Our empirical study shows that the TS-TVEC model is an effective tool that is capable of modeling the complexity of stochastic traffic flow processes and potentially applicable to real time traffic state prediction.

A demand analysis for the Japanese cod markets with unknown structural changes

We estimated the demands of Japan’s cod and pollock markets, taking into account unknown structural changes. We used monthly wholesale price and quantity data between January 2004 and July 2013 at the Tsukiji market in Tokyo, Japan. Structural break tests detected multiple structural changes in the targeted period. A price-dependent demand model with detected structural changes successfully cleaned up the effects of the structural changes and estimated an accurate demand and elasticities. When structural changes were taken into account, the model fits improved by up to 17 points. The results indicated the one-way influence of cod on the pollock market. Also, the pollock market was sensitive to an economic recession, but household income did not affect pollock as well as cod, presumably because both goods are necessary goods for Japanese consumers. These results are helpful for policy makers in devising marketing strategies. Since fresh cod is an inelastic good, cod fisheries can reduce their cost by reducing their days at sea, which would increase the cod price due to its inelastic characteristics. As such, they can expect some profit via cost reduction and an increase in the price.

How Actin Initiates the Motor Activity of Myosin

Fundamental to cellular processes are directional movements driven by molecular motors. A common theme for these and other molecular machines driven by ATP is that controlled release of hydrolysis products is essential to use the chemical energy efficiently. Mechanochemical transduction by myosin motors on actin is coupled to unknown structural changes that result in the sequential release of inorganic phosphate (Pi) and MgADP. We will describe how key regions of the motor play a role at the actin interface to trigger different transitions during the powerstroke. We will also present a myosin structure that explains how actin initiates force generation and movement. This structure possesses an actin interface that differs from previously seen pre- and post-stroke states of the motor. The structure also describe the tunnel (back door) that creates an escape route for Pi with a minimal rotation of the myosin lever arm that drives movements. Functional studies allow us to propose that this state represents the beginning of the powerstroke on actin, and that Pi translocation from the nucleotide pocket triggered by actin binding initiates force generation by myosin. This elucidates a strategy that may be common to many molecular machines.

Issues in Estimating New Keynesian Phillips Curves in the Presence of Unknown Structural Change

Many articles which have estimated models with forward looking expectations have reported that the magnitude of the coefficients of the expectations term is very large when compared with the effects coming from past dynamics. This has sometimes been regarded as implausible and led to the feeling that the expectations coefficient is biased upwards. A relatively general argument that has been advanced is that the bias could be due to structural changes in the means of the variables entering the structural equation. An alternative explanation is that the bias comes from weak instruments. In this article, we investigate the issue of upward bias in the estimated coefficients of the expectations variable based on a model where we can see what causes the breaks and how to control for them. We conclude that weak instruments are the most likely cause of any bias and note that structural change can affect the quality of instruments. We also look at some empirical work in Castle et al. (2014) on the new Kaynesian Phillips curve (NYPC) in the Euro Area and U.S. assessing whether the smaller coefficient on expectations that Castle et al. (2014) highlight is due to structural change. Our conclusion is that it is not. Instead it comes from their addition of variables to the NKPC. After allowing for the fact that there are weak instruments in the estimated re-specified model, it would seem that the forward coefficient estimate is actually quite high rather than low.

The relationship between body mass index and footprint parameters in older people

BackgroundThe relationship of body mass index (BMI) with footprint parameters has been studied in paediatric populations, but there are limited data regarding the effects of BMI on parameters in the elderly. ObjectivesTo establish the relationship between BMI and static footprint parameters in the elderly population. Methods128 subjects aged 65 and above with no history of lower extremity surgical intervention and no significant lower extremity weakness were included in the current study. BMI and footprint parameters of arch angle, Chippaux–Smirak index (CSI), Staheli index (SI), arch index (AI) and footprint index (FI) were measured for each subject, and statistical analysis was done to investigate the correlation between BMI and the parameters. ResultsWeak correlations detected between all calculated indices and angles with BMI, except the left foot arch angle. CSI, SI and AI of the right foot were found to be positively correlated with BMI, while a negative correlation between the arch angle and FI of right foot was shown with BMI. ConclusionsThe results reveal a relationship between BMI and footprint parameters that are indicative of flatfoot in the elderly. This could be due either to confounding of the footprint measure by fat or possibly due to an as yet unknown structural change that requires further evaluation.

Peptide-Conjugation Induced Conformational Changes in Human IgG1 Observed by Optimized Negative-Staining and Individual-Particle Electron Tomography

Peptides show much promise as potent and selective drug candidates. Fusing peptides to a scaffold monoclonal antibody produces a conjugated antibody which has the advantages of peptide activity yet also has the pharmacokinetics determined by the scaffold antibody. However, the conjugated antibody often has poor binding affinity to antigens that may be related to unknown structural changes. The study of the conformational change is difficult by conventional techniques because structural fluctuation under equilibrium results in multiple structures co-existing. Here, we employed our two recently developed electron microscopy (EM) techniques: optimized negative-staining (OpNS) EM and individual-particle electron tomography (IPET). Two-dimensional (2D) image analyses and three-dimensional (3D) maps have shown that the domains of antibodies present an elongated peptide-conjugated conformational change, suggesting that our EM techniques may be novel tools to monitor the structural conformation changes in heterogeneous and dynamic macromolecules, such as drug delivery vehicles after pharmacological synthesis and development.

Issues in Estimating New Keynesian Phillips Curves in the Presence of Unknown Structural Change

Many papers which have estimated models with forward looking expectations have reported that the magnitude of the coefficients of the expectations term is very large when compared with the effects coming from past dynamics. This has sometimes been regarded as implausible and led to the feeling that the expectations coefficient is biased upwards. A relatively general argument that has been advanced is that the bias could be due to structural changes in the means of the variables entering the structural equation. An alternative explanation is that the bias comes from weak instruments. In this paper we investigate the issue of upward bias in the estimated coefficients of the expectations variable based on a model where we can see what causes the breaks and how to control for them. We conclude that weak instruments are the most likely cause of any bias and note that structural change can affect the quality of instruments. We look at some empirical work in Castle et al. (2011) on the NK Phillips curve in the Euro Area. We assess whether the smaller coefficient on expectations that Castle et al. (2011) note when an allowance is made for structural change is due to weak instruments or to specification issues. Our finding suggests that it is probably a consequence of weak instruments.

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Structural changes and unit roots in non-stationary time series

The problem of testing hypotheses of a unit root and a structural change in one-dimensional time series is considered. A non-parametric two-step method for solution of the problem is proposed. The method is based upon the modified Kolmogorov–Smirnov statistic. At the first step of this method the hypothesis of stationarity of an obtained sample is tested against a unified alternative of a statistical non-stationarity of a time series (a unit root or a structural change). At the second step of the proposed method, in case of rejecting the stationarity hypothesis at the first step, the hypothesis of an unknown structural change is tested against the alternative of a unit root. We prove that probabilities of errors (false classification of hypotheses) of the proposed method converge to zero as the sample size tends to infinity.

Revisiting structural change and market integration in late 19th century American capital markets

This article draws on a variety of time series tools to more deeply explore issues surrounding the emergence of a national capital market in the late 19th century. Our focus is on the timing of the emergence of a national capital market. Rather than relying on the absolute narrowing of regional interest rate differentials, which is a common approach in this literature, we use Gregory and Hansen cointegration tests, which allow us not only to test for cointegrating relations in the interest rate series but to identify unknown structural change dates as a byproduct. We also use dynamic conditional correlations to determine the dates at which regional interest rate correlations began increasing. Our results suggest that structural changes are centred around the year 1900, which is consistent with Sylla's argument that the lowering of capital requirements by the Gold Standard Act of 1900 increased bank entry and competition and facilitated regional capital market convergence.