Improved Subset Autoregression: With R Package

The FitAR R (R Development Core Team 2008) package that is available on the Comprehensive R Archive Network is described. This package provides a comprehensive approach to fitting autoregressive and subset autoregressive time series. For long time series with complicated autocorrelation behavior, such as the monthly sunspot numbers, subset autoregression may prove more feasible and/or parsimonious than using AR or ARMA models. The two principal functions in this package are SelectModel and FitAR for automatic model selection and model fitting respectively. In addition to the regular autoregressive model and the usual subset autoregressive models (Tong'77), these functions implement a new family of models. This new family of subset autoregressive models is obtained by using the partial autocorrelations as parameters and then selecting a subset of these parameters. Further properties and results for these models are discussed in McLeod and Zhang (2006). The advantages of this approach are that not only is an efficient algorithm for exact maximum likelihood implemented but that efficient methods are derived for selecting high-order subset models that may occur in massive datasets containing long time series. A new improved extended {BIC} criterion, {UBIC}, developed by Chen and Chen (2008) is implemented for subset model selection. A complete suite of model building functions for each of the three types of autoregressive models described above are included in the package. The package includes functions for time series plots, diagnostic testing and plotting, bootstrapping, simulation, forecasting, Box-Cox analysis, spectral density estimation and other useful time series procedures. As well as methods for standard generic functions including print, plot, predict and others, some new generic functions and methods are supplied that make it easier to work with the output from FitAR for bootstrapping, simulation, spectral density estimation and Box-Cox analysis.

Algorithms for Linear Time Series Analysis: With R Package

Our ltsa package implements the Durbin-Levinson and Trench algorithms and provides a general approach to the problems of fitting, forecasting and simulating linear time series models as well as fitting regression models with linear time series errors. For computational efficiency both algorithms are implemented in C and interfaced to R. Examples are given which illustrate the efficiency and accuracy of the algorithms. We provide a second package FGN which illustrates the use of the ltsa package with fractional Gaussian noise (FGN). It is hoped that the ltsa will provide a base for further time series software.

Improved Subset Autoregression: With R Package

The FitAR R (R Development Core Team 2008) package that is available on the Comprehensive R Archive Network is described. This package provides a comprehensive approach to fitting autoregressive and subset autoregressive time series. For long time series with complicated autocorrelation behavior, such as the monthly sunspot numbers, subset autoregression may prove more feasible and/or parsimonious than using AR or ARMA models. The two principal functions in this package are SelectModel and FitAR for automatic model selection and model fitting respectively. In addition to the regular autoregressive model and the usual subset autoregressive models (Tong 1977), these functions implement a new family of models. This new family of subset autoregressive models is obtained by using the partial autocorrelations as parameters and then selecting a subset of these parameters. Further properties and results for these models are discussed in McLeod and Zhang (2006). The advantages of this approach are that not only is an efficient algorithm for exact maximum likelihood implemented but that efficient methods are derived for selecting high-order subset models that may occur in massive datasets containing long time series. A new improved extended {BIC} criterion, {UBIC}, developed by Chen and Chen (2008) is implemented for subset model selection. A complete suite of model building functions for each of the three types of autoregressive models described above are included in the package. The package includes functions for time series plots, diagnostic testing and plotting, bootstrapping, simulation, forecasting, Box-Cox analysis, spectral density estimation and other useful time series procedures. As well as methods for standard generic functions including print, plot, predict and others, some new generic functions and methods are supplied that make it easier to work with the output from FitAR for bootstrapping, simulation, spectral density estimation and Box-Cox analysis

Algorithms for Linear Time Series Analysis: With R Package

Our ltsa package implements the Durbin-Levinson and Trench algorithms and provides a general approach to the problems of fitting, forecasting and simulating linear time series models as well as fitting regression models with linear time series errors. For computational efficiency both algorithms are implemented in C and interfaced to R. Examples are given which illustrate the efficiency and accuracy of the algorithms. We provide a second package FGN which illustrates the use of the ltsa package with fractional Gaussian noise (FGN). It is hoped that the ltsa will provide a base for further time series software

Systematic reappraisal of marsh-orchids native to Scotland

© The Author(s), 2023.This article is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.Summary: The intensively studied Eurasian orchid genus Dactylorhiza has become a model system for exploring allopolyploid evolution, yet determining the optimal circumscriptions of, and most appropriate ranks for, its constituent taxa remain highly controversial topics. Here, novel allozyme data and detailed morphometric data for 16 Scottish marsh-orchid populations are interpreted in the context of recent DNA sequencing studies. Despite being derived from the same pair of parental species, the two allopolyploid species that currently occur in Scotland can reliably be distinguished using allozymes, haplotypes, ribotypes or sequences of nuclear genes. A modest range of diverse morphological characters are shown to distinguish the two molecularly-circumscribed species, but they have in the past been obscured by equivalent levels of infraspecific variation in characters rooted in anthocyanin pigments; these characters are better employed for distinguishing infraspecific taxa. Dactylorhiza francis-drucei (formerly D. traunsteinerioides) is confirmed as being distinct from the continental D. traunsteineri/lapponica, probably originating through allopatric isolation once the continental lineage reached Britain. All Scottish populations are attributed to the comparatively small-flowered, anthocyanin-rich subsp. francis-drucei, which includes as a variety the former D. 'ebudensis'; the less anthocyanin-rich subsp. traunsteinerioides is confined to Ireland, North Wales and northern England. In contrast with D. francis-drucei, only a minority of Scottish populations of D. purpurella are attributed to the anthocyanin-rich race, var. cambrensis. This species most likely originated through an allopolyploidy event that occurred comparatively recently within the British Isles, as it contains allozyme alleles distinctive of British rather than continental D. incarnata (its diploid pollen-parent). In contrast, the rare Scottish population of D. incarnata subsp. cruenta shares with its Irish counterparts a continental genotype, and is most likely a recent arrival in Scotland through long-distance dispersal. Among all European allotetraploid dactylorchids, D. purpurella is the species that most closely resembles D. incarnata, both molecularly and morphologically.Peer reviewe

Sea-weeding: Manual removal of macroalgae facilitates rapid coral recovery

Coral reef ecosystems globally are under threat, leading to declining coral cover and macroalgal proliferation. Manually removing macroalgae (i.e. ‘sea-weeding’) may promote local-scale coral recovery by reducing a biological barrier, though the impact of removal on community composition of benthic reef organisms has not been quantified. In this three-year study (2018–2021), fleshy macroalgae (predominantly Sargassum spp.) were periodically removed from 25 m2 experimental plots on two inshore fringing reefs of Yunbenun (Magnetic Island) in the central Great Barrier Reef. By the end of the study, coral cover in removal plots (n = 12 plots) assessed through in-field transects increased by at least 47% (2019 mean: 25.5%, 2021 mean: 37.4%), and macroalgal cover decreased by more than half. In contrast, in control plots (n = 12 plots), there was no change in macroalgal cover while coral cover remained stable (2019 mean: 16.4%, 2021 mean: 13.6%). Changes in benthic cover were supported by photoquadrat data, with Bayesian probability modelling indicating a 100% likelihood that coral cover more than doubled in removal plots over the study period, compared to only a 29% chance of increased coral cover in control plots. Synthesis and applications. Manual macroalgal removal can provide rapid benefits and enhance inshore coral reef recovery. Through involvement of community groups and citizen scientists, larger scale removal of macroalgae is a low-tech, high-impact, and achievable method for local reef management

SPARKLE (Subtypes of ischaemic stroke classification system), Incorporating measurement of carotid plaque burden: A new validated tool for the classification of ischemic stroke subtypes

Background: Previous classification systems of acute ischemic stroke (Causative Classification System, CCS, of acute ischemic stroke, Trial of Org 10172 in Acute Stroke Treatment, TOAST) established the diagnosis of large artery disease (LAD) based on the presence or absence of carotid stenosis. However, carotid plaque burden is a stronger predictor of cardiovascular risk than stenosis. Our objective was to update definitions of ischemic stroke subtypes to improve the detection of LAD and to assess the validity and reliability of a new classification system: SPARKLE (Subtypes of Ischaemic Stroke Classification System). Methods: In a retrospective review of clinical research data, we compared three stroke subtype classifications: CCS, TOAST and SPARKLE. We analyzed a random sample of 275 patients presenting with minor stroke or transient ischemic attack (TIA) in an Urgent TIA Clinic in London, Ont., Canada, between 2002 and 2012. Results: There was substantial overall agreement between SPARKLE and CCS (κ = 0.75), with significant differences in the rate of detection of LAD, cardioembolic and undetermined causes of stroke or TIA. The inter-rater reliability of SPARKLE was substantial (κ = 0.76) and the intra-rater reliability was excellent (κ = 0.91). Conclusion: SPARKLE is a valid and reliable classification system, providing advantages compared to CCS and TOAST. The incorporation of plaque burden into the classification of LAD increases the proportion of cases attributable to LAD and reduces the proportion classified as being of \u27undetermined\u27 etiology. © 2014 S. Karger AG, Basel

Interaction of estrogen receptor α with proliferating cell nuclear antigen

The ability of estrogen receptor α (ERα) to modulate gene expression is influenced by the recruitment of a host of co-regulatory proteins to target genes. To further understand how estrogen-responsive genes are regulated, we have isolated and identified proteins associated with ERα when it is bound to DNA containing the consensus estrogen response element (ERE). One of the proteins identified in this complex, proliferating cell nuclear antigen (PCNA), is required for DNA replication and repair. We show that PCNA interacts with ERα in the absence and in the presence of DNA, enhances the interaction of ERα with ERE-containing DNA, and associates with endogenous estrogen-responsive genes. Interestingly, rather than altering hormone responsiveness of endogenous, estrogen-responsive genes, PCNA increases the basal expression of these genes. Our studies suggest that in addition to serving as a platform for the recruitment of DNA replication and repair proteins, PCNA may serve as a platform for transcription factors involved in regulating gene expression

Treating patients as persons : a capabilities approach to support delivery of person-centered care

Peer reviewedPublisher PD