9 research outputs found

    Long memory and changepoint models:a spectral classification procedure

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    Time series within fields such as finance and economics are often modelled using long memory processes. Alternative studies on the same data can suggest that series may actually contain a ‚Äėchangepoint‚Äô (a point within the time series where the data generating process has changed). These models have been shown to have elements of similarity, such as within their spectrum. Without prior knowledge this leads to an ambiguity between these two models, meaning it is difficult to assess which model is most appropriate. We demonstrate that considering this problem in a time-varying environment using the time-varying spectrum removes this ambiguity. Using the wavelet spectrum, we then use a classification approach to determine the most appropriate model (long memory or changepoint). Simulation results are presented across a number of models followed by an application to stock cross-correlations and US inflation. The results indicate that the proposed classification outperforms an existing hypothesis testing approach on a number of models and performs comparatively across others

    Distinguishing trends and shifts from memory in climate data

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    The detection of climate change and its attribution to the corresponding underlying processes is challenging because signals such as trends and shifts are superposed on variability arising from the memory within the climate system. Statistical methods used to characterize change in time series must be flexible enough to distinguish these components. Here we propose an approach tailored to distinguish these different modes of change by fitting a series of models and selecting the most suitable one according to an information criterion. The models involve combinations of a constant mean or a trend superposed to a background of white noise with or without autocorrelation to characterize the memory, and are able to detect multiple changepoints in each model configuration. Through a simulation study on synthetic time series, the approach is shown to be effective in distinguishing abrupt changes from trends and memory by identifying the true number and timing of abrupt changes when they are present. Furthermore, the proposed method is better performing than two commonly used approaches for the detection of abrupt changes in climate time series. Using this approach, the so-called hiatus in recent global mean surface warming fails to be detected as a shift in the rate of temperature rise but is instead consistent with steady increase since the 1960s/1970s. Our method also supports the hypothesis that the Pacific decadal oscillation behaves as a short-memory process rather than forced mean shifts as previously suggested. These examples demonstrate the usefulness of the proposed approach for change detection and for avoiding the most pervasive types of mistake in the detection of climate change. © 2018 American Meteorological Society

    Strategy Selection versus Flexibility:Using Eye-trackers to Investigate Strategy Use during Mental Rotation

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    Spatial researchers have been arguing over the optimum cognitive strategy for spatial problem-solving for several decades. The current article aims to shift this debate from strategy dichotomies to strategy flexibility-a cognitive process, which although alluded to in spatial research, presents practical methodological challenges to empirical testing. In the current study, participants' eye movements were tracked during a mental rotation task (MRT) using the Tobii x60 eye-tracker. Results of a latent profile analysis, combining different eye movement parameters, indicated two distinct eye-patterns-fixating and switching patterns. The switching eye-pattern was associated with high mental rotation performance. There were no sex differences in eye-patterns. To investigate strategy flexibility, we used a novel application of the changepoint detection algorithm on eye movement data. Strategy flexibility significantly predicted mental rotation performance. Male participants demonstrated higher strategy flexibility than did female participants. Our findings highlight the importance of strategy flexibility in spatial thinking and have implications for designing spatial training techniques. The novel approaches to analyzing eye movement data in the current paper can be extended to research beyond the spatial domain

    COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

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    BACKGROUND: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. METHODS: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FINDINGS: Among 57‚ÄČ032‚ÄČ174 individuals included in the cohort, 13‚ÄČ990‚ÄČ423 COVID-19 events were identified in 7‚ÄČ244‚ÄČ925 individuals, equating to an infection rate of 12¬∑7% during the study period. Of 7‚ÄČ244‚ÄČ925 individuals, 460‚ÄČ737 (6¬∑4%) were admitted to hospital and 158‚ÄČ020 (2¬∑2%) died. Of 460‚ÄČ737 individuals who were admitted to hospital, 48‚ÄČ847 (10¬∑6%) were admitted to the intensive care unit (ICU), 69‚ÄČ090 (15¬∑0%) received non-invasive ventilation, and 25‚ÄČ928 (5¬∑6%) received invasive ventilation. Among 384‚ÄČ135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23‚ÄČ485 [30¬∑4%] of 77‚ÄČ202 patients) than wave 2 (44‚ÄČ220 [23¬∑1%] of 191‚ÄČ528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50¬∑7%] of 5063 patients). 15‚ÄČ486 (9¬∑8%) of 158‚ÄČ020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10‚ÄČ884 (6¬∑9%) of 158‚ÄČ020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. INTERPRETATION: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. FUNDING: British Heart Foundation Data Science Centre, led by Health Data Research UK

    An original phylogenetic approach identified mitochondrial haplogroup T1a1 as inversely associated with breast cancer risk in BRCA2 mutation carriers

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    Introduction: Individuals carrying pathogenic mutations in the BRCA1 and BRCA2 genes have a high lifetime risk of breast cancer. BRCA1 and BRCA2 are involved in DNA double-strand break repair, DNA alterations that can be caused by exposure to reactive oxygen species, a main source of which are mitochondria. Mitochondrial genome variations affect electron transport chain efficiency and reactive oxygen species production. Individuals with different mitochondrial haplogroups differ in their metabolism and sensitivity to oxidative stress. Variability in mitochondrial genetic background can alter reactive oxygen species production, leading to cancer risk. In the present study, we tested the hypothesis that mitochondrial haplogroups modify breast cancer risk in BRCA1/2 mutation carriers. Methods: We genotyped 22,214 (11,421 affected, 10,793 unaffected) mutation carriers belonging to the Consortium of Investigators of Modifiers of BRCA1/2 for 129 mitochondrial polymorphisms using the iCOGS array. Haplogroup inference and association detection were performed using a phylogenetic approach. ALTree was applied to explore the reference mitochondrial evolutionary tree and detect subclades enriched in affected or unaffected individuals. Results: We discovered that subclade T1a1 was depleted in affected BRCA2 mutation carriers compared with the rest of clade T (hazard ratio (HR) = 0.55; 95% confidence interval (CI), 0.34 to 0.88; P = 0.01). Compared with the most frequent haplogroup in the general population (that is, H and T clades), the T1a1 haplogroup has a HR of 0.62 (95% CI, 0.40 to 0.95; P = 0.03). We also identified three potential susceptibility loci, including G13708A/rs28359178, which has demonstrated an inverse association with familial breast cancer risk. Conclusions: This study illustrates how original approaches such as the phylogeny-based method we used can empower classical molecular epidemiological studies aimed at identifying association or risk modification effects.Peer reviewe

    Loss of BIN1 protein in Alzheimer’s disease promotes synaptic accumulation of phosphorylated tau and disrupts tau release:Tau-directed effects of BIN1 loss in AD

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    Polymorphisms associated with BIN1 (bridging integrator 1) confer the second greatest risk for developing late-onset Alzheimer‚Äôs disease. The biological consequences of this genetic variation are not fully understood; however, BIN1 is a binding partner for tau. Tau is normally a highly soluble cytoplasmic protein, but in Alzheimer‚Äôs disease, tau is abnormally phosphorylated and accumulates at synapses to exert synaptotoxicity. The purpose of this study was to determine whether alterations in BIN1 and tau in Alzheimer‚Äôs disease promote the damaging redistribution of tau to synapses, as a mechanism by which BIN1 polymorphisms may increase the risk of developing Alzheimer‚Äôs disease. We show that BIN1 is lost from the cytoplasmic fraction of Alzheimer‚Äôs disease cortex, and this is accompanied by the progressive mislocalization of phosphorylated tau to synapses. We confirmed proline 216 in tau as critical for tau interaction with the BIN1-SH3 domain and showed that the phosphorylation of tau disrupts this binding, suggesting that tau phosphorylation in Alzheimer‚Äôs disease disrupts tau‚ÄďBIN1 associations. Moreover, we show that BIN1 knockdown in rat primary neurons to mimic BIN1 loss in Alzheimer‚Äôs disease brain causes the damaging accumulation of phosphorylated tau at synapses and alterations in dendritic spine morphology. We also observed reduced release of tau from neurons upon BIN1 silencing, suggesting that BIN1 loss disrupts the function of extracellular tau. Together, these data indicate that polymorphisms associated with BIN1 that reduce BIN1 protein levels in the brain likely act synergistically with increased tau phosphorylation to increase the risk of Alzheimer‚Äôs disease by disrupting cytoplasmic tau‚ÄďBIN1 interactions, promoting the damaging mis-sorting of phosphorylated tau to synapses to alter synapse structure and reducing the release of physiological forms of tau to disrupt tau function

    Regional climate of the Larsen B embayment 1980-2014

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    Understanding the climate response of the Antarctic Peninsula ice sheet is vital for accurate predictions of sea level rise. However, since climate models are typically too coarse to capture spatial variability in local scale meteorological processes, our ability to study specific sectors has been limited by the local fidelity of such models and the (often sparse) availability of observations. We show that a high-resolution (5.5 km x 5.5 km) version of a regional climate model (RACMO2.3) can reproduce observed inter-annual variability in the Larsen B embayment sufficiently to enable its use in investigating long-term changes in this sector. Using the model, together with AWS data, we confirm previous findings that the year of the Larsen B ice shelf collapse (2001/2002) was a strong melt year, but discover that total annual melt production was in fact ~30% lower than two years prior. While the year before collapse exhibited the lowest melting and highest snowfall during 1980-2014, the ice shelf was likely pre-conditioned for collapse by a series of strong melt years in the 1990s. Melt energy has since returned to pre-1990s levels, which likely explains the lack of further significant collapse in the region (e.g. of SCAR Inlet)