Local polynomial Whittle estimation of perturbed fractional processes

We propose a semiparametric local polynomial Whittle with noise estimator of the memory parameter in long memory time series perturbed by a noise term which may be serially correlated. The estimator approximates the log-spectrum of the short-memory component of the signal as well as that of the perturbation by two separate polynomials. Including these polynomials we obtain a reduction in the order of magnitude of the bias, but also inflate the asymptotic variance of the long memory estimator by a multiplicative constant. We show that the estimator is consistent for d in (0,1), asymptotically normal for d in (0,3/4), and if the spectral density is sufficiently smooth near frequency zero, the rate of convergence can become arbitrarily close to the parametric rate, sqrt(n). A Monte Carlo study reveals that the proposed estimator performs well in the presence of a serially correlated perturbation term. Furthermore, an empirical investigation of the 30 DJIA stocks shows that this estimator indicates stronger persistence in volatility than the standard local Whittle (with noise) estimator.Bias reduction, local Whittle, long memory, perturbed fractional process, semiparametric estimation, stochastic volatility

A vector autoregressive model for electricity prices subject to long memory and regime switching

A regime dependent VAR model is suggested that allows long memory (fractional integration) in each of the observed regime states as well as the possibility of fractional cointegration. The model is motivated by the dynamics of electricity prices where the transmission of power is subject to occasional congestion periods. For a system of bilateral prices non-congestion means that electricity prices are identical whereas congestion makes prices depart. Hence, the joint price dynamics implies switching between a univariate price process under non-congestion and a bivariate price process under congestion. At the same time, it is an empirical regularity that electricity prices tend to show a high degree of long memory, and thus that prices may be fractionally cointegrated. Analysis of Nord Pool data shows that even though the prices are identical under non-congestion, the prices are not, in general, fractionally cointegrated in the congestion state. Hence, in most cases price convergence is a property following from regime switching rather than a conventional error correction mechanism. Finally, the suggested model is shown to deliver forecasts that are more precise compared to competing models.Cointegration, electricity prices, fractional integration, long memory, regime switching

Field emissions of N2O during biomass production may affect the sustainability of agro-biofuels

Field emissions of N2O during cultivation of bioenergy crops may counterbalance a considerable part of the avoided fossil CO2 emissions that are achieved by fossil fuel displacemen

Administrative burden reduction over time: Literature review, trends and gap analysis

Burden reduction is a key issue in modern public administrations’ and businesses’ agendas. Compliance with mandatory regulations can have a direct impact on a country’s economic performance, growth, and development. Research in this area, contributes to a better understanding of the implications and context of administrative burden, and increases the efficiency of the strategies adopted to reduce it. The goal of this study is to undertake a review of the current state of the art on Administrative Burden Reduction (ABR), in order to gain a deeper insight about the subject, identify current gaps, and better plan for future research. A total of 122 papers were identified as relevant, out of a pool of 742 papers retrieved from the current literature. The relevant papers were analyzed across four dimensions: methodology, type and focus, and targeted stakeholders. Three key gaps were identified and discussed in relation to: citizen orientated services and burden reduction; empirical research and post-initiative re-evaluation; and, the role of stakeholders, interest groups and end-users in driving ABR. Lastly a conceptual framework model and next steps are proposed.“SmartEGOV: Harnessing EGOV for Smart Governance (Foundations, methods, Tools) / NORTE-01-0145-FEDER-000037”, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (EFDR

Protein⁻Protein Interactions with Connexin 43: Regulation and Function

Connexins are integral membrane building blocks that form gap junctions, enabling direct cytoplasmic exchange of ions and low-molecular-mass metabolites between adjacent cells. In the heart, gap junctions mediate the propagation of cardiac action potentials and the maintenance of a regular beating rhythm. A number of connexin interacting proteins have been described and are known gap junction regulators either through direct effects (e.g., kinases) or the formation of larger multifunctional complexes (e.g., cytoskeleton scaffold proteins). Most connexin partners can be categorized as either proteins promoting coupling by stimulating forward trafficking and channel opening or inhibiting coupling by inducing channel closure, internalization, and degradation. While some interactions have only been implied through co-localization using immunohistochemistry, others have been confirmed by biophysical methods that allow detection of a direct interaction. Our understanding of these interactions is, by far, most well developed for connexin 43 (Cx43) and the scope of this review is to summarize our current knowledge of their functional and regulatory roles. The significance of these interactions is further exemplified by demonstrating their importance at the intercalated disc, a major hub for Cx43 regulation and Cx43 mediated effects

Systematic Characterisation of Cellular Localisation and Expression Profiles of Proteins Containing MHC Ligands

Presentation of peptides on Major Histocompatibility Complex (MHC) molecules is the cornerstone in immune system activation and increased knowledge of the characteristics of MHC ligands and their source proteins is highly desirable.In the present large-scale study, we used a large data set of proteins containing experimentally identified MHC class I or II ligands and examined the proteins according to their expression profiles at the mRNA level and their Gene Ontology (GO) classification within the cellular component ontology. Proteins encoded by highly abundant mRNA were found to be much more likely to be the source of MHC ligands. Of the 2.5% most abundant mRNAs as much as 41% of the proteins encoded by these mRNAs contained MHC class I ligands. For proteins containing MHC class II ligands, the corresponding percentage was 11%. Furthermore, we found that most proteins containing MHC class I ligands were localised to the intracellular parts of the cell including the cytoplasm and nucleus. MHC class II ligand donors were, on the other hand, mostly membrane proteins.The results contribute to the ongoing debate concerning the nature of MHC ligand-containing proteins and can be used to extend the existing methods for MHC ligand predictions by including the source protein's localisation and expression profile. Improving the current methods is important in the growing quest for epitopes that can be used for vaccine or diagnostic purposes, especially when it comes to large DNA viruses and cancer

Unsupervised Idealization of Ion Channel Recordings by Minimum Description Length:Application to Human PIEZO1-Channels

Researchers can investigate the mechanistic and molecular basis of many physiological phenomena in cells by analyzing the fundamental properties of single ion channels. These analyses entail recording single channel currents and measuring current amplitudes and transition rates between conductance states. Since most electrophysiological recordings contain noise, the data analysis can proceed by idealizing the recordings to isolate the true currents from the noise. This de-noising can be accomplished with threshold crossing algorithms and Hidden Markov Models, but such procedures generally depend on inputs and supervision by the user, thus requiring some prior knowledge of underlying processes. Channels with unknown gating and/or functional sub-states and the presence in the recording of currents from uncorrelated background channels present substantial challenges to such analyses. Here we describe and characterize an idealization algorithm based on Rissanen's Minimum Description Length (MDL) Principle. This method uses minimal assumptions and idealizes ion channel recordings without requiring a detailed user input or a priori assumptions about channel conductance and kinetics. Furthermore, we demonstrate that correlation analysis of conductance steps can resolve properties of single ion channels in recordings contaminated by signals from multiple channels. We first validated our methods on simulated data defined with a range of different signal-to-noise levels, and then showed that our algorithm can recover channel currents and their substates from recordings with multiple channels, even under conditions of high noise. We then tested the MDL algorithm on real experimental data from human PIEZO1 channels and found that our method revealed the presence of substates with alternate conductances