Search Results
Cardio-respiratory coordination increases during sleep apnea
2014, Riedl, M., Müller, A., Kraemer, J.F., Penzel, T., Kurths, J., Wessel, N.
Cardiovascular diseases are the main source of morbidity and mortality in the United States with costs of more than $170 billion. Repetitive respiratory disorders during sleep are assumed to be a major cause of these diseases. Therefore, the understanding of the cardio-respiratory regulation during these events is of high public interest. One of the governing mechanisms is the mutual influence of the cardiac and respiratory oscillations on their respective onsets, the cardiorespiratory coordination (CRC). We analyze this mechanism based on nocturnal measurements of 27 males suffering from obstructive sleep apnea syndrome. Here we find, by using an advanced analysis technique, the coordigram, not only that the occurrence of CRC is significantly more frequent during respiratory sleep disturbances than in normal respiration (p-value<10-51) but also more frequent after these events (p-value<10-15). Especially, the latter finding contradicts the common assumption that spontaneous CRC can only be observed in epochs of relaxed conditions, while our newly discovered epochs of CRC after disturbances are characterized by high autonomic stress. Our findings on the connection between CRC and the appearance of sleep-disordered events require a substantial extension of the current understanding of obstructive sleep apneas and hypopneas.
Learning from urban form to predict building heights
2020, Milojevic-DupontI, Nikola, Hans, Nicolai, Kaack, Lynn H., Zumwald, Marius, Andrieux, François, de Barros Soares, Daniel, Lohrey, Steffen, PichlerI, Peter-Paul, Creutzig, Felix
Understanding cities as complex systems, sustainable urban planning depends on reliable high-resolution data, for example of the building stock to upscale region-wide retrofit policies. For some cities and regions, these data exist in detailed 3D models based on real-world measurements. However, they are still expensive to build and maintain, a significant challenge, especially for small and medium-sized cities that are home to the majority of the European population. New methods are needed to estimate relevant building stock characteristics reliably and cost-effectively. Here, we present a machine learning based method for predicting building heights, which is based only on open-access geospatial data on urban form, such as building footprints and street networks. The method allows to predict building heights for regions where no dedicated 3D models exist currently. We train our model using building data from four European countries (France, Italy, the Netherlands, and Germany) and find that the morphology of the urban fabric surrounding a given building is highly predictive of the height of the building. A test on the German state of Brandenburg shows that our model predicts building heights with an average error well below the typical floor height (about 2.5 m), without having access to training data from Germany. Furthermore, we show that even a small amount of local height data obtained by citizens substantially improves the prediction accuracy. Our results illustrate the possibility of predicting missing data on urban infrastructure; they also underline the value of open government data and volunteered geographic information for scientific applications, such as contextual but scalable strategies to mitigate climate change.
Fishing-induced life-history changes degrade and destabilize harvested ecosystems
2016, Kuparinen, A., Boit, A., Valdovinos, F.S., Lassaux, H., Martinez, N.D.
Sleep apnea-hypopnea quantification by cardiovascular data analysis
2014, Camargo, S., Riedl, M., Anteneodo, C., Kurths, J., Penzel, T., Wessel, N.
Sleep disorders are a major risk factor for cardiovascular diseases. Sleep apnea is the most common sleep disturbance and its detection relies on a polysomnography, i.e., a combination of several medical examinations performed during a monitored sleep night. In order to detect occurrences of sleep apnea without the need of combined recordings, we focus our efforts on extracting a quantifier related to the events of sleep apnea from a cardiovascular time series, namely systolic blood pressure (SBP). Physiologic time series are generally highly nonstationary and entrap the application of conventional tools that require a stationary condition. In our study, data nonstationarities are uncovered by a segmentation procedure which splits the signal into stationary patches, providing local quantities such as mean and variance of the SBP signal in each stationary patch, as well as its duration L. We analysed the data of 26 apneic diagnosed individuals, divided into hypertensive and normotensive groups, and compared the results with those of a control group. From the segmentation procedure, we identified that the average duration 〈L〉, as well as the average variance 〈σ2〉, are correlated to the apnea-hypoapnea index (AHI), previously obtained by polysomnographic exams. Moreover, our results unveil an oscillatory pattern in apneic subjects, whose amplitude S∗ is also correlated with AHI. All these quantities allow to separate apneic individuals, with an accuracy of at least 79%. Therefore, they provide alternative criteria to detect sleep apnea based on a single time series, the systolic blood pressure.
Global, regional, and national burden of mortality associated with non-optimal ambient temperatures from 2000 to 2019: a three-stage modelling study
2021, Zhao, Qi, Guo, Yuming, Ye, Tingting, Gasparrini, Antonio, Tong, Shilu, Overcenco, Ala, Urban, Aleš, Schneider, Alexandra, Entezari, Alireza, Vicedo-Cabrera, Ana Maria, Zanobetti, Antonella, Analitis, Antonis, Zeka, Ariana, Tobias, Aurelio, Nunes, Baltazar, Alahmad, Barrak, Armstrong, Ben, Forsberg, Bertil, Pan, Shih-Chun, Íñiguez, Carmen, Ameling, Caroline, De la Cruz Valencia, César, Åström, Christofer, Houthuijs, Danny, Dung, Do Van, Royé, Dominic, Indermitte, Ene, Lavigne, Eric, Mayvaneh, Fatemeh, Acquaotta, Fiorella, de'Donato, Francesca, Di Ruscio, Francesco, Sera, Francesco, Carrasco-Escobar, Gabriel, Kan, Haidong, Orru, Hans, Kim, Ho, Holobaca, Iulian-Horia, Kyselý, Jan, Madureira, Joana, Schwartz, Joel, Jaakkola, Jouni J. K., Katsouyanni, Klea, Hurtado Diaz, Magali, Ragettli, Martina S., Hashizume, Masahiro, Pascal, Mathilde, de Sousa Zanotti Stagliorio Coélho, Micheline, Valdés Ortega, Nicolás, Ryti, Niilo, Scovronick, Noah, Michelozzi, Paola, Matus Correa, Patricia, Goodman, Patrick, Nascimento Saldiva, Paulo Hilario, Abrutzky, Rosana, Osorio, Samuel, Rao, Shilpa, Fratianni, Simona, Dang, Tran Ngoc, Colistro, Valentina, Huber, Veronika, Lee, Whanhee, Seposo, Xerxes, Honda, Yasushi, Guo, Yue Leon, Bell, Michelle L., Li, Shanshan
Background: Exposure to cold or hot temperatures is associated with premature deaths. We aimed to evaluate the global, regional, and national mortality burden associated with non-optimal ambient temperatures. Methods: In this modelling study, we collected time-series data on mortality and ambient temperatures from 750 locations in 43 countries and five meta-predictors at a grid size of 0·5° × 0·5° across the globe. A three-stage analysis strategy was used. First, the temperature–mortality association was fitted for each location by use of a time-series regression. Second, a multivariate meta-regression model was built between location-specific estimates and meta-predictors. Finally, the grid-specific temperature–mortality association between 2000 and 2019 was predicted by use of the fitted meta-regression and the grid-specific meta-predictors. Excess deaths due to non-optimal temperatures, the ratio between annual excess deaths and all deaths of a year (the excess death ratio), and the death rate per 100 000 residents were then calculated for each grid across the world. Grids were divided according to regional groupings of the UN Statistics Division. Findings: Globally, 5 083 173 deaths (95% empirical CI [eCI] 4 087 967–5 965 520) were associated with non-optimal temperatures per year, accounting for 9·43% (95% eCI 7·58–11·07) of all deaths (8·52% [6·19–10·47] were cold-related and 0·91% [0·56–1·36] were heat-related). There were 74 temperature-related excess deaths per 100 000 residents (95% eCI 60–87). The mortality burden varied geographically. Of all excess deaths, 2 617 322 (51·49%) occurred in Asia. Eastern Europe had the highest heat-related excess death rate and Sub-Saharan Africa had the highest cold-related excess death rate. From 2000–03 to 2016–19, the global cold-related excess death ratio changed by −0·51 percentage points (95% eCI −0·61 to −0·42) and the global heat-related excess death ratio increased by 0·21 percentage points (0·13–0·31), leading to a net reduction in the overall ratio. The largest decline in overall excess death ratio occurred in South-eastern Asia, whereas excess death ratio fluctuated in Southern Asia and Europe. Interpretation: Non-optimal temperatures are associated with a substantial mortality burden, which varies spatiotemporally. Our findings will benefit international, national, and local communities in developing preparedness and prevention strategies to reduce weather-related impacts immediately and under climate change scenarios. Funding: Australian Research Council and the Australian National Health and Medical Research Council. © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
Order patterns networks (orpan) - A method to estimate time-evolving functional connectivity from multivariate time series
2012, Schinkel, S., Zamora-López, G., Dimigen, O., Sommer, W., Kurths, J.
Complex networks provide an excellent framework for studying the function of the human brain activity. Yet estimating functional networks from measured signals is not trivial, especially if the data is non-stationary and noisy as it is often the case with physiological recordings. In this article we propose a method that uses the local rank structure of the data to define functional links in terms of identical rank structures. The method yields temporal sequences of networks which permits to trace the evolution of the functional connectivity during the time course of the observation. We demonstrate the potentials of this approach with model data as well as with experimental data from an electrophysiological study on language processing.
A network-based microfoundation of Granovetter’s threshold model for social tipping
2020, Wiedermann, Marc, Smith, E. Keith, Heitzig, Jobst, Donges, Jonathan F.
Social tipping, where minorities trigger larger populations to engage in collective action, has been suggested as one key aspect in addressing contemporary global challenges. Here, we refine Granovetter’s widely acknowledged theoretical threshold model of collective behavior as a numerical modelling tool for understanding social tipping processes and resolve issues that so far have hindered such applications. Based on real-world observations and social movement theory, we group the population into certain or potential actors, such that – in contrast to its original formulation – the model predicts non-trivial final shares of acting individuals. Then, we use a network cascade model to explain and analytically derive that previously hypothesized broad threshold distributions emerge if individuals become active via social interaction. Thus, through intuitive parameters and low dimensionality our refined model is adaptable to explain the likelihood of engaging in collective behavior where social-tipping-like processes emerge as saddle-node bifurcations and hysteresis. © 2020, The Author(s).