A biomarker-validated time scale in years of disease progression has identified early- and late-onset subgroups in sporadic Alzheimer's disease

BACKGROUND

It is possible to calculate the number of years to the expected clinical onset (YECO) of autosomal-dominant Alzheimer's disease (adAD). A similar time scale is lacking for sporadic Alzheimer's disease (sAD). The purpose was to design and validate a time scale in YECO for patients with sAD in relation to CSF and PET biomarkers.

METHODS

Patients diagnosed with Alzheimer's disease (AD, n = 48) or mild cognitive impairment (MCI, n = 46) participated in the study. They underwent a standardized clinical examination at the Memory clinic, Karolinska University Hospital, Stockholm, Sweden, which included present and previous medical history, laboratory screening, cognitive assessment, CSF biomarkers (Aβ₄₂, total-tau, and p-tau), and an MRI of the brain. They were also assessed with two PET tracers, ¹¹C-Pittsburgh compound B and ¹⁸F-fluorodeoxyglucose. Assuming concordance of cognitive decline in sAD and adAD, YECO for these patients was calculated using equations for the relationship between cognitive performance, YECO, and years of education in adAD (Almkvist et al. J Int Neuropsychol Soc 23:195-203, 2017).

RESULTS

The mean current point of disease progression was 3.2 years after the estimated clinical onset in patients with sAD and 3.4 years prior to the estimated clinical onset in patients with MCI, as indicated by the median YECO from five cognitive tests. The associations between YECO and biomarkers were significant, while those between chronological age and biomarkers were nonsignificant. The estimated disease onset (chronological age minus YECO) followed a bimodal distribution with frequency maxima before (early-onset) and after (late-onset) 65 years of age. The early- and late-onset subgroups differed significantly in biomarkers and cognition, but after control for YECO, this difference disappeared for all except the APOE e4 gene (more frequent in early- than in late-onset).

CONCLUSIONS

A novel time scale in years of disease progression based on cognition was designed and validated in patients with AD using CSF and PET biomarkers. Two early- and late-disease onset subgroups were identified differing with respect to APOE e4.

Response of cross-correlations between high PM2.5 and O3 with increasing time scales to the COVID-19: different trends in BTH and PRD

The air pollution in China currently is characterized by high fine particulate matter (PM_2.5) and ozone (O₃) concentrations. Compared with single high pollution events, such double high pollution (DHP) events (both PM_2.5 and O₃ are above the National Ambient Air Quality Standards (NAAQS)) pose a greater threat to public health and environment. In 2020, the outbreak of COVID-19 provided a special time window to further understand the cross-correlation between PM_2.5 and O₃. Based on this background, a novel detrended cross-correlation analysis (DCCA) based on maximum time series of variable time scales (VM-DCCA) method is established in this paper to compare the cross-correlation between high PM_2.5 and O₃ in Beijing-Tianjin-Heibei (BTH) and Pearl River Delta (PRD). At first, the results show that PM_2.5 decreased while O₃ increased in most cities due to the effect of COVID-19, and the increase in O₃ is more significant in PRD than in BTH. Secondly, through DCCA, the results show that the PM_2.5-O₃ DCCA exponents α decrease by an average of 4.40% and 2.35% in BTH and PRD respectively during COVID-19 period compared with non-COVID-19 period. Further, through VM-DCCA, the results show that the PM_2.5-O₃ VM-DCCA exponents [Formula: see text] in PRD weaken rapidly with the increase of time scales, with decline range of about 23.53% and 22.90% during the non-COVID-19 period and COVID-19 period respectively at 28-h time scale. BTH is completely different. Without significant tendency, its [Formula: see text] is always higher than that in PRD at different time scales. Finally, we explain the above results with the self-organized criticality (SOC) theory. The impact of meteorological conditions and atmospheric oxidation capacity (AOC) variation during the COVID-19 period on SOC state are further discussed. The results show that the characteristics of cross-correlation between high PM_2.5 and O₃ are the manifestation of the SOC theory of atmospheric system. Relevant conclusions are important for the establishment of regionally targeted PM_2.5-O₃ DHP coordinated control strategies.

A sentiment-based modeling and analysis of stock price during the COVID-19: U- and Swoosh-shaped recovery

In the aftermath of stock market crash due to COVID-19, not all sectors recovered in the same way. Recently, a stock price model is proposed by Mahata et al. (2021) that describes V- and L-shaped recovery of the stocks and indices, but fails to simulate the U- and Swoosh-shaped recovery that arises due to sharp fall, continuation at the low price and followed by quick recovery, slow recovery for longer period, respectively. We propose a modified model by introducing a new parameter θ = + 1 , 0 , - 1 to quantify investors' positive, neutral and negative sentiments, respectively. The model explains movement of sectoral indices with positive financial anti-fragility ( ϕ ) showing U- and Swoosh-shaped recovery. Simulation using synthetic fund-flow with different shock lengths, ϕ , negative sentiment period and portion of fund-flow during recovery period show U- and Swoosh-shaped recovery. It shows that recovery of indices with positive ϕ becomes very weak with extended shock and negative sentiment period. Stocks with higher ϕ and fund-flow show quick recovery. Simulation of Nifty Bank, Nifty Financial and Nifty Realty show U-shaped recovery and Nifty IT shows Swoosh-shaped recovery. Simulation results are consistent with stock price movement. The estimated time-scale of shock and recovery of these indices are also consistent with the time duration of change of negative sentiment from the onset of COVID-19. We conclude that investors need to evaluate sentiment along with ϕ before investing in stock markets because negative sentiment can dampen the recovery even in financially anti-fragile stocks.

Is gold a hedge or safe haven against oil and currency market movements? A revisit using multifractal approach

We investigate gold's role as a hedge or safe haven against oil price and currency movements across calm and extreme market conditions. For the empirical analysis, we extend the intraday multifractal correlation measure developed by Madani et al. (Bankers, Markets & Investors, 163:2-13, 2020) to consider the dependence for calm and extreme movement periods across different time scales. Interestingly, we employ the rolling window method to examine the time-varying dependence between gold-oil and gold-currency in terms of calm and turmoil market conditions. Based on high frequency (5-min intervals) across the period 2017-2019, our analysis shows three interesting findings. First, gold acts as a weak (strong) hedge for oil (currency) market movements, across all agent types. Second, gold has strong safe-haven capability against extreme currency movements, and against only short time scales of oil price movements. Third, hedging strategies confirm the scale-dependent gold's role in reducing portfolio risk as a hedge or safe haven. Implications for investors, financial institutions, and policymakers are discussed.

Updating a Paleogene magnetobiochronological time scale through graphical integration

All studies focused on the evaluation of paleoecological variability over geological time must be linked to a specific age or time interval, which can be defined using different time scales (biostratigraphic, chronostratigraphic, geochronological or orbital). Therefore, integrated time scales are essential to allow comparisons of data from different locations and/or to assess evolutionary and other events through time. Here we use a new method to update a Paleogene magnetobiochronological time scale, with the following contributions:•

The update of the Paleogene magnetobiochronological scale was made by graphical correlation with new age models and adding calcareous nannoplankton and planktonic foraminiferal biozones from different authors.

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An excel file structure was proposed to plot any kind of data in MATLAB software, as long as they are associated with some of the scales shown in our updated version of Paleogene magnetobiochronology.

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The excel file structure facilitates the analysis of long-term trends of taxonomic groups throughout the Paleogene, and of their evolution in a period characterized by intense climate variability.

Modeling and analysis of the effect of COVID-19 on the stock price: V and L-shape recovery

The emergence of the COVID-19 pandemic, a new and novel risk factor, leads to the stock price crash due to the investors' rapid and synchronous sell-off. However, within a short period, the quality sectors start recovering from the bottom. A stock price model has been developed to capture the price dynamics during shock and recovery phases of such crisis. The main variable and parameter of the model are the net fund flow ( Ψ t ) due to institutional investors, and financial antifragility ( ϕ ) of a company, respectively. We assume that during the crash, the stock price fall is independent of the ϕ . We study the effects of shock length ( T S ) and ϕ on the stock price during the crisis period using the Ψ t obtained from both the synthetic fund flow data and real fund flow data. We observed that the possibility of recovery of stock with ϕ > 0 , termed as quality stock, decreases with an increase in T S beyond a specific period. A quality stock with higher ϕ shows V-shape recovery and outperform others. The T S and recovery period of quality stock are almost equal in the Indian market. Financially stressed stocks, i.e., the stocks with ϕ < 0 , show L-shape recovery during the pandemic. The stock data and model analysis show that the investors, in the uncertainty like COVID-19, invest in the quality stocks to restructure their portfolio to reduce the risk. The study may help the investors to make the right investment decision during a crisis.

New discrete-time fractional derivatives based on the bilinear transformation: Definitions and properties

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The paper introduces new discrete-time derivative concepts based on the bilinear transformation.

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Forward and backward derivatives having a high degree of similarity with the usual continuous-time Grunwald-Letnikov derivatives are introduced.

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Corresponding linear discrete-time systems are defined.

In this paper we introduce new discrete-time derivative concepts based on the bilinear (Tustin) transformation. From the new formulation, we obtain derivatives that exhibit a high degree of similarity with the continuous-time Grünwald-Letnikov derivatives. Their properties are described highlighting one important feature, namely that such derivatives have always long memory.

Periodic density as an endpoint of customized plankton community responses to petroleum hydrocarbons: A level of toxic effect should be matched with a suitable time scale

As an endpoint of community response to contaminants, average periodic density of populations (APDP) has been introduced to model species interactions in a community with 4 planktonic species. An ecological model for the community was developed by means of interspecific relationship including competition and predation to calculate the APDP. As a case study, we reported here the ecotoxicological effects of petroleum hydrocarbons (PHC) collected from Bohai oil field on densities of two algae, Platymonas subcordiformis and Isochrysis galbana, a rotifer, Brachionus plicatilis, and of a cladocera, Penilia avirostris, in single species and a microcosm experiment. Time scales expressing toxic effect increased with increasing levels of toxic effect from molecule to community. Remarkable periodic changes in densities were found during the tests in microcosm experiment, revealing a strong species reaction. The minimum time scale characterizing toxic effect at a community level should be the common cycle of population densities of the microcosm. In addition, the cycles of plankton densities shortened in general with increasing PHC, showing an evident toxic effect on the microcosm. Using APDP as the endpoint, a threshold concentration for the modeled microcosm was calculated to be 0.404 mg-PHC L^-1. The APDP was found to be more sensitive and reliable than the standing crops of populations as the endpoint. This indicated that the APDP, an endpoint at the community level, could be quantitatively related to the endpoints at the population level, and led to the quantitative concentration-toxic effect relationship at the community level.

Stability of delayed inertial neural networks on time scales: A unified matrix-measure approach

This note introduces a unified matrix-measure concept to study the stability of a class of inertial neural networks with bounded time delays on time scales. The novel matrix-measure concept unifies the classic matrix-measure and the generalized matrix-measure concept. One sufficient global exponential stability criterion is obtained based on this key matrix-measure and no Lyapunov function is required. To make the stability performance better, another stability criterion in which more detailed information is involved has been acquired. The theoretical results in this note contain and extend some existing continuous-time and discrete-time works. A numerical example is given to show the validity of the results.

Analyzing the significant environmental factors on the spatial and temporal distribution of water quality utilizing multivariate statistical techniques: a case study in the Balihe Lake, China

The assessment of surface water quality is significant to the management of aquatic ecosystem. In this research, in Balihe Lake which is an agricultural watershed lake, 11 environmental parameters (pH, water temperature, water depth, turbidity, DO, COD, TN, NH₄⁺-N, NO₃^--N, TP, Chl-a) are monitored at 45 sampling sites in four seasons (winter of 2016, spring, summer, and autumn of 2017). With these monitoring data, two kinds of multivariate statistical methods including cluster analysis (CA) and principal component analysis (PCA) are applied to evaluate the spatial and temporal characteristics of the surface water quality. The results reveal that the spatial clusters (less, moderately, and highly polluted sections) of 45 sampling sites classified by the CA method are exactly consistent with the geographical distribution of these sampling sites, which rely on water quality meliorating downstream. From the perspective of time scale, the correlations between environmental parameters generated by the PCA method reveal that the main factors affecting the surface water quality are different in the four seasons. For the whole study period, which is a longer time scale rather than season, the main factors are also different to that of any season. Large time scale may weaken the effect and potential risk of nutrients on water quality, and it is therefore reasonable to select seasonal scale for the study of water quality in an agricultural watershed by using PCA. The results of this research may demonstrate significance to the identification of the main pollution factors and water quality assessment in freshwater lake with multivariate statistical methods.

On some nonlinear retarded Volterra-Fredholm type integral inequalities on time scales and their applications

In this paper, we establish some new nonlinear retarded Volterra-Fredholm type integral inequalities on time scales. Our results not only generalize and extend some known integral inequalities, but also provide a handy and effective tool for the study of qualitative properties of solutions of some Volterra-Fredholm type dynamic equations.

What Has the Study of Digital Games Contributed to the Science of Expert Behavior?

I review the historical context for modeling skilled performance in games. Using Newell's (1990) concept of time bands for explaining cognitive behavior, I categorize the current papers in terms of time scales, type of data, and analysis methodologies. I discuss strengths and weaknesses of these approaches for describing skill acquisition and why the study of digital games can address the challenges of replication and generalizability. Cognitive science needs to pay closer attention to population representativeness to enhance generalizability of findings, and to the social band of explanation, in order to explain why so few individuals reach expert levels of performance.

The effect of increasing lifespan and recycling rate on carbon storage in wood products from theoretical model to application for the European wood sector

The use of wood products is often promoted as a climate change mitigation option to reduce atmospheric carbon dioxide concentrations. In previous literature, we identified longevity and recycling rate as two determining factors that influence the carbon stock in wood products, but no studies have predicted the effect of improved wood use on carbon storage over time. In this study, we aimed at evaluating changes in the lifespan and the recycling rate as two options for enhancing carbon stock in wood products for different time horizons. We first explored the behaviour over time of both factors in a theoretical simulation, and then calculated their effect for the European wood sector of the future. The theoretical simulation shows that the carbon stock in wood products increases linearly when increasing the average lifespan of wood products and exponentially when improving the recycling rate. The emissions savings under the current use of wood products in Europe in 2030 were estimated at 57.65 Mt carbon dioxide (CO₂) per year. This amount could be increased 5 Mt CO₂ if average lifespan increased 19.54 % or if recycling rate increased 20.92 % in 2017. However, the combination of both strategies could increase the emissions saving almost 5 Mt CO₂ more by 2030. Incrementing recycling rate of paper and paperboard is the best short-term strategy (2030) to reduce emissions, but elongating average lifespan of wood-based panels is a better strategy for longer term periods (2046).

Simplification of irreversible Markov chains by removal of states with fast leaving rates

In the recent work of Ullah et al. (2012a), the authors developed an effective method to simplify reversible Markov chains by removal of states with low equilibrium occupancies. In this paper, we extend this result to irreversible Markov chains. We show that an irreversible chain can be simplified by removal of states with fast leaving rates. Moreover, we reveal that the irreversibility of the chain will always decrease after model simplification. This suggests that although model simplification can retain almost all the dynamic information of the chain, it will lose some thermodynamic information as a trade-off. Examples from biology are also given to illustrate the main results of this paper.

Multi-scale modelling of bioreactor-separator system for wastewater treatment with two-dimensional activated sludge floc dynamics

A simple "first generation" multi-scale computational model of the formation of activated sludge flocs at micro-scale and reactor performance at macro-scale is proposed. The model couples mass balances for substrates and biomass at reactor scale with an individual-based approach for the floc morphology, shape and micro-colony development. Among the novel model processes included are the group attachment/detachment of micro-flocs to the core structure and the clustering of nitrifiers. Simulation results qualitatively describe the formation of micro-colonies of ammonia and nitrite oxidizers and the extracellular polymeric substance produced by heterotrophic microorganisms, as typically observed in fluorescence in situ hybridization images. These results are the first step towards realistic multi-scale multispecies models of the activated sludge wastewater treatment systems and a generic modelling strategy that could be extended to other engineered biological systems.

Impact of immortal person-time and time scale in comparative effectiveness research for medical devices: a case for implantable cardioverter-defibrillators

OBJECTIVES

To assess the extent of immortal time bias in estimating the clinical effectiveness of implantable cardioverter-defibrillators (ICDs) and the impact of methods of handling immortal time bias.

STUDY DESIGN AND SETTING

Retrospective population-based cohort study of patients with heart failure in a national registry linked to Medicare claims (2003-2008). We compared three methods of handling immortal time bias, namely the Mantel-Byar (or time-dependent exposure assignment), the landmark, and the exclusion methods.

RESULTS

Of the 5,226 study patients, 1,274 (24.4%) received ICD therapy. Total person-years in the Mantel-Byar method were 2,639, or 490 more than that in the exclusion method, reflecting potential immortal time in the study. The exclusion method yielded a hazard ratio of 0.71 (95% confidence interval [CI]: 0.63-0.80), which was 16% lower than the Mantel-Byar method (0.84; 95% CI: 0.75-0.95). The 120-day landmark method yielded similar results to those produced by the Mantel-Byar method (0.82; 95% CI: 0.72-0.95).

CONCLUSIONS

Immortal time bias was detected in the ICD clinical effectiveness study, which might have led to substantial bias overestimating the treatment effect if handled by exclusion. When an appropriate landmark was selected, that method yielded similar hazard ratios to those obtained by the Mantel-Byar method, supporting the validity of the landmark method.