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Data-driven prediction and prevention of extreme events in a spatially extended excitable system
(2015)
The stimulation and dominance of potentially harmful phytoplankton taxa at a given locale and time are determined by local environmental conditions as well as by transport to or from neighboring regions. The present study investigated the occurrence of common harmful algal bloom (HAB) taxa within the Southern California Bight, using cross-correlation functions to determine potential dependencies between HAB taxa and environmental factors, and potential links to algal transport via local hydrography and currents. A simulation study, in which Lagrangian particles were released, was used to assess travel times due to advection by prevailing ocean currents in the bight. Our results indicate that transport of some taxa may be an important mechanism for the expansion of their distributions into other regions, which was supported by mean travel times derived from our simulation study and other literature on ocean currents in the Southern California Bight. In other cases, however, phytoplankton dynamics were rather linked to local environmental conditions, including coastal upwelling events. Overall, our study shows that complex current patterns in the Southern California Bight may contribute significantly to the formation and expansion of HABs in addition to local environmental factors determining the spatiotemporal dynamics of phytoplankton blooms.
We consider recent reports on small-world topologies of interaction networks derived from the dynamics of spatially extended systems that are investigated in diverse scientific fields such as neurosciences, geophysics, or meteorology. With numerical simulations that mimic typical experimental situations, we have identified an important constraint when characterizing such networks: indications of a small-world topology can be expected solely due to the spatial sampling of the system along with the commonly used time series analysis based approaches to network characterization.
The network approach towards the analysis of the dynamics of complex systems has been successfully applied in a multitude of studies in the neurosciences and has yielded fascinating insights. With this approach, a complex system is considered to be composed of different constituents which interact with each other. Interaction structures can be compactly represented in interaction networks. In this contribution, we present a brief overview about how interaction networks are derived from multivariate time series, about basic network characteristics, and about challenges associated with this analysis approach.
Rationale: Previous studies [Topolnik et al., Cereb Cortex 2003; 13: 883; Schindler et al., Brain 2007; 130: 65] indicate that the termination of focal onset seizures may be causally related to an increase of global neuronal correlation during the second half of the seizures. This increase was observed to occur earlier in complex partial seizures than in secondarily generalized seizures. We here address the question whether such an increase of neuronal correlation prior to seizure end is indeed a global phenomenon, involving both hemispheres or whether there are side-specific differences. Methods: We analyzed 20 focal onset seizures (10 complex partial, 10 secondarily generalized seizures) recorded in 13 patients who underwent presurgical evaluation of focal epilepsies of different origin. EEG was recorded intracranially from bilaterally implanted subdural strip and intrahippocampal depth electrodes. Utilizing a moving window approach, we investigated the evolution of the maximum cross correlation for all channel combinations during seizures. For each moving window the mean value of the maximum cross correlation (MCC) between all electrode contacts was computed separately for each hemisphere. After normalization of seizure durations, MCC values of the ipsi- and contralateral hemisphere for all seizures were determined. Results: We observed that the MCC of the contralateral hemisphere in complex partial seizures increased during the first half of the seizure, whereas, for the same time interval, the MCC of the ipsilateral hemisphere even declined below the level of the pre-seizure period. In contrast, no significant differences between both hemispheres could be observed for secondarily generalized seizures where both hemispheres showed a simultaneous increase of MCC during the second half of the seizures. The level of MCC for the contralateral hemisphere was higher for complex partial seizures than for secondarily generalized seizures during the first half of the seizure. Conclusions: Our findings indicate that there are indeed lateralized differences in the evolution of global neuronal correlation during complex partial and secondarily generalized seizures. The observed contralateral increase of neuronal correlation during complex partial seizures might indicate an emerging self-organizing mechanism for preventing the spread of seizure activity.
We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures – known for their complex spatial and temporal dynamics – we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.
Using the OpenSim software and verified anatomical data, a computer model for the calculation of biomechanical parameters is developed and used to determine the effect of a reattachment of the Supraspinatus muscle with a medial displacement of the muscle attachment point, which may be necessary for a rupture of the supraspinatus tendon. The results include the influence of the operation on basic biomechanical parameters such as the lever arm, as well as the calculated the muscle activations for the supraspinatus and deltoid. In addition, the influence on joint stability is examined by an analysis of the joint reaction force. The study provides a detailed description of the used model, as well as medical findings to a reattachment of the supraspinatus.
Mit der Software OpenSim und überprüften anatomischen Daten wird ein Computermodell zur Berechnung von biomechanischen Parametern entwickelt und genutzt, um den Effekt einer Refixierung des Supraspinatusmuskels mit einer medialen Verschiebung des Muskelansatzpunktes zu ermitteln, wie sie unter anderem nach einem Riss der Supraspinatussehne notwendig sein kann. Die Ergebnisse umfassen hierbei den Einfluss der Operation auf grundlegende biomechanische Parameter wie den Hebelarm sowie die berechneten Muskelaktivierungen für den Supraspinatus und Deltoideus. Zusätzlich wird der Einfluss auf die Gelenkstabilität betrachtet und durch eine Analyse der Gelenkreaktionskraft untersucht. Die Studie bietet eine detaillierte Beschreibung des genutzten Modells, sowie medizinische Erkenntnisse zu einer Refixierung des Supraspinatus.
Reliable methods for automatic readability assessment have the potential to impact a variety of fields, ranging from machine translation to self-informed learning. Recently, large language models for the German language (such as GBERT and GPT-2-Wechsel) have become available, allowing to develop Deep Learning based approaches that promise to further improve automatic readability assessment. In this contribution, we studied the ability of ensembles of fine-tuned GBERT and GPT-2-Wechsel models to reliably predict the readability of German sentences. We combined these models with linguistic features and investigated the dependence of prediction performance on ensemble size and composition. Mixed ensembles of GBERT and GPT-2-Wechsel performed better than ensembles of the same size consisting of only GBERT or GPT-2-Wechsel models. Our models were evaluated in the GermEval 2022 Shared Task on Text Complexity Assessment on data of German sentences. On out-of-sample data, our best ensemble achieved a root mean squared error of 0:435.
The MYOTONES experiment is the first to monitor changes in the basic biomechanical properties (tone, elasticity and stiffness) of the resting human myofascial system due to microgravity with a oninvasive, portable device on board the ISS. The MyotonPRO device applies several brief mechanical stimuli to the surface of the skin, and the natural oscillation signals of the tissue beneath are detected and computed by the MyotonPRO. Thus, an objective, quick and easy determination of the state of the underlying tissue is possible.
Two preflight, four inflight and four post flight measurements were performed on a male astronaut using the same 10 measurement points (MP) for each session. MPs were located on the plantar fascia, Achilles tendon, M. soleus, M. gastrocnemius, M. multifidus, M. splenius capitis, M. deltoideus anterior, M. rectus femoris, infrapatellar tendon, M. tibialis anterior. Subcutaneous tissues thickness above the MPs was measured using ultrasound imaging. Magnetic resonance images (MRI) of lower limb muscles and functional tests were also performed pre- and postflight.
Our first measurements on board the ISS confirmed increased tone and stiffness of the lumbar multifidus muscle, an important trunk stabilizer, dysfunction of which is known to be associated with back pain. Furthermore, reduced tone and stiffness of Achilles tendon and plantar fascia were observed inflight vs. preflight, confirming previous findings from terrestrial analog studies and parabolic
flights. Unexpectedly, the deltoid showed negative inflight changes in tone and stiffness, and increased elasticity, suggesting a potential risk of muscle atrophy in longer spaceflight that should be addressed by adequate inflight countermeasure protocols. Most values from limb and back MPS showed deflected patterns (in either directions) from inflight shortly after the re-entry phase on the landing day and one week later. Most parameter values then normalized to baseline after 3 weeks likely due to 1G re-adaptation and possible outcome of the reconditioning protocol. No major changes in subcutaneous tissues thickness above the MPs were found inflight vs preflight, suggesting no bias (i.e., fluid shift, extreme tissue thickening or loss). Pre- and postflight MRI and functional tests showed negligible changes in calf muscle size, power and force, which is likely due to training effects from current inflight exercise protocols.
The MYOTONES experiment is currently ongoing to collect data from further crew members. The potential impact of this research is to better understand the effects of microgravity and countermeasures over the time course of an ISS mission cycle. This will enable exercise countermeasures to be tailored
Clearance of blood components and fluid drainage play a crucial role in subarachnoid hemorrhage (SAH) and post hemorrhagic hydrocephalus (PHH). With the involvement of interstitial fluid (ISF) and cerebrospinal fluid (CSF), two pathways for the clearance of fluid and solutes in the brain are proposed. Starting at the level of capillaries, flow of ISF follows along the basement membranes in the walls of cerebral arteries out of the parenchyma to drain into the lymphatics and CSF [1]–[3]. Conversely, it is shown that CSF enters the parenchyma between glial and pial basement membranes of penetrating arteries [4]–[6]. Nevertheless, the involved structures and the contribution of either flow pathway to fluid balance between the subarachnoid space and interstitial space remains controversial. Low frequency oscillations in vascular tone are referred to as vasomotion and corresponding vasomotion waves are modeled as the driving force for flow of ISF out of the parenchyma [7]. Retinal vessel analysis (RVA) allows non-invasive measurement of retinal vessel vasomotion with respect to diameter changes [8]. Thus, the aim of the study is to investigate vasomotion in RVA signals of SAH and PHH patients.