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Volatility is a widely recognized measure of market risk. As volatility is not observed it has to be estimated from market prices, i.e., as the implied volatility from option prices. The volatility index VIX making volatility a tradeable asset in its own right is computed from near- and next-term put and call options on the S&P 500 with more than 23 days and less than 37 days to expiration and non-vanishing bid. In the present paper we quantify the information content of the constituents of the VIX about the volatility of the S&P 500 in terms of the Fisher information matrix. Assuming that observed option prices are centered on the theoretical price provided by Heston's model perturbed by additive Gaussian noise we relate their Fisher information matrix to the Greeks in the Heston model. We find that the prices of options contained in the VIX basket allow for reliable estimates of the volatility of the S&P 500 with negligible uncertainty as long as volatility is large enough. Interestingly, if volatility drops below a critical value of roughly 3%, inferences from option prices become imprecise because Vega, the derivative of a European option w.r.t. volatility, and thereby the Fisher information nearly vanishes.
The goal of heavy ion reactions at low beam energies is to explore the QCD phase diagram at high net baryon chemical potential. To relate experimental observations with a first order phase transition or a critical endpoint, dynamical approaches for the theoretical description have to be developed. In this summary of the corresponding plenary talk, the status of the dynamical modeling including the most recent advances is presented. The remaining challenges are highlighted and promising experimental measurements are pointed out.
Surface color and predictability determine contextual modulation of V1 firing and gamma oscillations
(2019)
The integration of direct bottom-up inputs with contextual information is a core feature of neocortical circuits. In area V1, neurons may reduce their firing rates when their receptive field input can be predicted by spatial context. Gamma-synchronized (30–80 Hz) firing may provide a complementary signal to rates, reflecting stronger synchronization between neuronal populations receiving mutually predictable inputs. We show that large uniform surfaces, which have high spatial predictability, strongly suppressed firing yet induced prominent gamma synchronization in macaque V1, particularly when they were colored. Yet, chromatic mismatches between center and surround, breaking predictability, strongly reduced gamma synchronization while increasing firing rates. Differences between responses to different colors, including strong gamma-responses to red, arose from stimulus adaptation to a full-screen background, suggesting prominent differences in adaptation between M- and L-cone signaling pathways. Thus, synchrony signaled whether RF inputs were predicted from spatial context, while firing rates increased when stimuli were unpredicted from context.
When a visual stimulus is repeated, average neuronal responses typically decrease, yet they might maintain or even increase their impact through increased synchronization. Previous work has found that many repetitions of a grating lead to increasing gamma-band synchronization. Here, we show in awake macaque area V1 that both repetition-related reductions in firing rate and increases in gamma are specific to the repeated stimulus. These effects show some persistence on the timescale of minutes. Gamma increases are specific to the presented stimulus location. Further, repetition effects on gamma and on firing rates generalize to images of natural objects. These findings support the notion that gamma-band synchronization subserves the adaptive processing of repeated stimulus encounters.
When a visual stimulus is repeated, average neuronal responses typically decrease, yet they might maintain or even increase their impact through increased synchronization. Previous work has found that many repetitions of a grating lead to increasing gamma-band synchronization. Here we show in awake macaque area V1 that both, repetition-related reductions in firing rate and increases in gamma are specific to the repeated stimulus. These effects showed some persistence on the timescale of minutes. Further, gamma increases were specific to the presented stimulus location. Importantly, repetition effects on gamma and on firing rates generalized to natural images. These findings suggest that gamma-band synchronization subserves the adaptive processing of repeated stimulus encounters, both for generating efficient stimulus responses and possibly for memory formation.
Background: The technical development of imaging techniques in life sciences has enabled the three-dimensional recording of living samples at increasing temporal resolutions. Dynamic 3D data sets of developing organisms allow for time-resolved quantitative analyses of morphogenetic changes in three dimensions, but require efficient and automatable analysis pipelines to tackle the resulting Terabytes of image data. Particle image velocimetry (PIV) is a robust and segmentation-free technique that is suitable for quantifying collective cellular migration on data sets with different labeling schemes. This paper presents the implementation of an efficient 3D PIV package using the Julia programming language—quickPIV. Our software is focused on optimizing CPU performance and ensuring the robustness of the PIV analyses on biological data.
Results: QuickPIV is three times faster than the Python implementation hosted in openPIV, both in 2D and 3D. Our software is also faster than the fastest 2D PIV package in openPIV, written in C++. The accuracy evaluation of our software on synthetic data agrees with the expected accuracies described in the literature. Additionally, by applying quickPIV to three data sets of the embryogenesis of Tribolium castaneum, we obtained vector fields that recapitulate the migration movements of gastrulation, both in nuclear and actin-labeled embryos. We show normalized squared error cross-correlation to be especially accurate in detecting translations in non-segmentable biological image data.
Conclusions: The presented software addresses the need for a fast and open-source 3D PIV package in biological research. Currently, quickPIV offers efficient 2D and 3D PIV analyses featuring zero-normalized and normalized squared error cross-correlations, sub-pixel/voxel approximation, and multi-pass. Post-processing options include filtering and averaging of the resulting vector fields, extraction of velocity, divergence and collectiveness maps, simulation of pseudo-trajectories, and unit conversion. In addition, our software includes functions to visualize the 3D vector fields in Paraview.
This a review of the present status of heavy-ion collisions at intermediate energies. The main goal of heavy-ion physics in this energy regime is to shed some light on the nuclear equation of state (EOS), hence we present the basic concept of the EOS in nuclear matter as well as of nuclear shock waves which provide the key mechanism for the compression of nuclear matter. The main part of this article is devoted to the models currently used for describing heavy-ion reactions theoretically and to the observables useful for extracting information about the EOS from experiments. A detailed discussion of the flow effects with a broad comparison with the avaible data is presented. The many-body aspects of such reactions are investigated via the multifragmentation break up of excited nuclear systems and a comparison of model calculations with the most recent multifragmentation experiments is presented.
Reprogramming of tomato leaf metabolome by the activity of heat stress transcription factor HsfB1
(2020)
Plants respond to high temperatures with global changes of the transcriptome, proteome, and metabolome. Heat stress transcription factors (Hsfs) are the core regulators of transcriptome responses as they control the reprogramming of expression of hundreds of genes. The thermotolerance-related function of Hsfs is mainly based on the regulation of many heat shock proteins (HSPs). Instead, the Hsf-dependent reprogramming of metabolic pathways and their contribution to thermotolerance are not well described. In tomato (Solanum lycopersicum), manipulation of HsfB1, either by suppression or overexpression (OE) leads to enhanced thermotolerance and coincides with distinct profile of metabolic routes based on a metabolome profiling of wild-type (WT) and HsfB1 transgenic plants. Leaves of HsfB1 knock-down plants show an accumulation of metabolites with a positive effect on thermotolerance such as the sugars sucrose and glucose and the polyamine putrescine. OE of HsfB1 leads to the accumulation of products of the phenylpropanoid and flavonoid pathways, including several caffeoyl quinic acid isomers. The latter is due to the enhanced transcription of genes coding key enzymes in both pathways, in some cases in both non-stressed and stressed plants. Our results show that beyond the control of the expression of Hsfs and HSPs, HsfB1 has a wider activity range by regulating important metabolic pathways providing an important link between stress response and physiological tomato development.
Stockpiling neuraminidase inhibitors (NAIs) such as oseltamivir and zanamivir is part of a global effort to be prepared for an influenza pandemic. However, the contribution of NAIs for treatment and prevention of influenza and its complications is largely debatable. Here, we developed a transparent mathematical modelling setting to analyse the impact of NAIs on influenza disease at within-host and population level. Analytical and simulation results indicate that even assuming unrealistically high efficacies for NAIs, drug intake starting on the onset of symptoms has a negligible effect on an individual's viral load and symptoms score. Increasing NAIs doses does not provide a better outcome as is generally believed. Considering Tamiflu's pandemic regimen for prophylaxis, different multiscale simulation scenarios reveal modest reductions in epidemic size despite high investments in stockpiling. Our results question the use of NAIs in general to treat influenza as well as the respective stockpiling by regulatory authorities.
Neuraminidase inhibitors in influenza treatment and prevention – is it time to call it a day?
(2018)
Stockpiling neuraminidase inhibitors (NAIs) such as oseltamivir and zanamivir is part of a global effort to be prepared for an influenza pandemic. However, the contribution of NAIs for the treatment and prevention of influenza and its complications is largely debatable due to constraints in the ability to control for confounders and to explore unobserved areas of the drug effects. For this study, we used a mathematical model of influenza infection which allowed transparent analyses. The model recreated the oseltamivir effects and indicated that: (i) the efficacy was limited by design, (ii) a 99% efficacy could be achieved by using high drug doses (however, taking high doses of drug 48 h post-infection could only yield a maximum of 1.6-day reduction in the time to symptom alleviation), and (iii) contributions of oseltamivir to epidemic control could be high, but were observed only in fragile settings. In a typical influenza infection, NAIs’ efficacy is inherently not high, and even if their efficacy is improved, the effect can be negligible in practice.