Neglected chaos in international stock markets:Bayesian analysis of the joint return-volatility dynamical system

We use a novel Bayesian inference procedure for the Lyapunov exponent in the dynamical system of returns and their unobserved volatility. In the dynamical system, computation of largest Lyapunov exponent by traditional methods is impossible as the stochastic nature has to be taken explicitly into account due to unobserved volatility. We apply the new techniques to daily stock return data for a group of six countries, namely USA, UK, Switzerland, Netherlands, Germany and France, from 2003 to 2014 by means of Sequential Monte Carlo for Bayesian inference. The evidence points to the direction that there is indeed noisy chaos both before and after the recent financial crisis. However, when a much simpler model is examined where the interaction between returns and volatility is not taken into consideration jointly, the hypothesis of chaotic dynamics does not receive much support by the data (“neglected chaos”)

Bayesian analysis of chaos: The joint return-volatility dynamical system

We use a novel Bayesian inference procedure for the Lyapunov exponent in the dynamical system of returns and their unobserved volatility. In the dynamical system, computation of largest Lyapunov exponent by tradi- tional methods is impossible as the stochastic nature has to be taken explicitly into account due to unobserved volatility. We apply the new techniques to daily stock return data for a group of six world countries, namely USA, UK, Switzerland, Netherlands, Germany and France, from 2003 to 2014 by means of Sequential Monte Carlo for Bayesian inference. The evidence points to the direction that there is indeed noisy chaos both before and after the recent financial crisis. However, when a much simpler model is examined where the interaction between returns and volatility is not taken into consideration jointly, the hypothesis of chaotic dynamics does not receive much support by the data (“neglected chaos”)

Bayesian analysis of chaos: The joint return-volatility dynamical system

We use a novel Bayesian inference procedure for the Lyapunov exponent in the dynamical system of returns and their unobserved volatility. In the dynamical system, computation of largest Lyapunov exponent by tradi- tional methods is impossible as the stochastic nature has to be taken explicitly into account due to unobserved volatility. We apply the new techniques to daily stock return data for a group of six world countries, namely USA, UK, Switzerland, Netherlands, Germany and France, from 2003 to 2014 by means of Sequential Monte Carlo for Bayesian inference. The evidence points to the direction that there is indeed noisy chaos both before and after the recent financial crisis. However, when a much simpler model is examined where the interaction between returns and volatility is not taken into consideration jointly, the hypothesis of chaotic dynamics does not receive much support by the data (“neglected chaos”)

Constrained 1,4-dialkylpiperazines as monoamine transporters inhibitors for cocaine-related abuse

Cocaine abuse and addiction remain grave health and societal problems with nearly 11,000 overdose deaths in 2015. Despite the high prevalence of cocaine use, no FDA-approved therapeutic for treating cocaine addiction has been achieved. The primary target for cocaine is dopamine transporter (DAT) and the rewarding and reinforcing effects of cocaine are mediated predominantly by its inhibition, with a consequent ‘reverse agonist’ effect. Several DAT inhibitors have been proposed as potential drugs for cocaine abuse.[1-2] One of the most studied DAT inhibitors, GBR12909 (Ki DAT = 3.7 nM), is able to slightly increase DA level and to blunt the cocaine-induced elevation of extracellular DA in vivo without exerting the central exciting effects of cocaine and addiction. Unfortunately, the phase I clinical trials failed, due to its cardiotoxicity.[3-4] In a lead optimisation program focused to identify novel and safe GBR12909 analogues, nine constrained derivatives were design and synthesized in a ligand based approach. Maintaining unaltered the fluoro-phenyl and phenylpropylpiperazine moiety, the rigidification of the ethylene ether, by means of tetrahydrofuran, dioxolane, dioxane, oxathiolane and dithiolane ring, was investigated in a focused SAR study (Fig. 1). All the compounds were assayed for the determination of the binding affinity for DAT, NET and SERT. In particular, two dioxolane derivatives displayed a binding affinity comparable to that of GBR12909, with Ki of 21.2 and 13.9 nM for DAT, and a selectivity ratio SERT/DAT > 30. Since the cyclization introduces one chiral centre, the two enantiomers of one racemic mixture were prepared following enantioselective synthetic procedures. The (R)- and (S)-forms showed a binding affinity comparable to the one determined for the racemate (Ki DAT of 16 and 46 nM, respectively), suggesting that the configuration of the stereocenters slightly affect the binding to the DAT transporter. For the most interesting derivatives, uptake inhibition assays were conducted in rat brain synaptosomes. It was observed that the potency trend parallel the affinity values

Scientific challenges of convective-scale numerical weather prediction

Numerical weather prediction (NWP) models are increasing in resolution and becoming capable of explicitly representing individual convective storms. Is this increase in resolution leading to better forecasts? Unfortunately, we do not have sufficient theoretical understanding about this weather regime to make full use of these NWPs. After extensive efforts over the course of a decade, convective–scale weather forecasts with horizontal grid spacings of 1–5 km are now operational at national weather services around the world, accompanied by ensemble prediction systems (EPSs). However, though already operational, the capacity of forecasts for this scale is still to be fully exploited by overcoming the fundamental difficulty in prediction: the fully three–dimensional and turbulent nature of the atmosphere. The prediction of this scale is totally different from that of the synoptic scale (103 km) with slowly–evolving semi–geostrophic dynamics and relatively long predictability on the order of a few days. Even theoretically, very little is understood about the convective scale compared to our extensive knowledge of the synoptic-scale weather regime as a partial–differential equation system, as well as in terms of the fluid mechanics, predictability, uncertainties, and stochasticity. Furthermore, there is a requirement for a drastic modification of data assimilation methodologies, physics (e.g., microphysics), parameterizations, as well as the numerics for use at the convective scale. We need to focus on more fundamental theoretical issues: the Liouville principle and Bayesian probability for probabilistic forecasts; and more fundamental turbulence research to provide robust numerics for the full variety of turbulent flows. The present essay reviews those basic theoretical challenges as comprehensibly as possible. The breadth of the problems that we face is a challenge in itself: an attempt to reduce these into a single critical agenda should be avoided

Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data.

Telomere length is a risk factor in disease and the dynamics of telomere length are crucial to our understanding of cell replication and vitality. The proliferation of whole genome sequencing represents an unprecedented opportunity to glean new insights into telomere biology on a previously unimaginable scale. To this end, a number of approaches for estimating telomere length from whole-genome sequencing data have been proposed. Here we present Telomerecat, a novel approach to the estimation of telomere length. Previous methods have been dependent on the number of telomeres present in a cell being known, which may be problematic when analysing aneuploid cancer data and non-human samples. Telomerecat is designed to be agnostic to the number of telomeres present, making it suited for the purpose of estimating telomere length in cancer studies. Telomerecat also accounts for interstitial telomeric reads and presents a novel approach to dealing with sequencing errors. We show that Telomerecat performs well at telomere length estimation when compared to leading experimental and computational methods. Furthermore, we show that it detects expected patterns in longitudinal data, repeated measurements, and cross-species comparisons. We also apply the method to a cancer cell data, uncovering an interesting relationship with the underlying telomerase genotype

Publisher Correction: Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper

Roux-en-Y Gastric Bypass Alters Brain Activity in Regions that Underlie Reward and Taste Perception.

BACKGROUND:Roux-en-Y gastric bypass (RYGB) surgery is a very effective bariatric procedure to achieve significant and sustained weight loss, yet little is known about the procedure's impact on the brain. This study examined the effects of RYGB on the brain's response to the anticipation of highly palatable versus regular food. METHODS:High fat diet-induced obese rats underwent RYGB or sham operation and were then tested for conditioned place preference (CPP) for the bacon-paired chamber, relative to the chow-paired chamber. After CPP, animals were placed in either chamber without the food stimulus, and brain-glucose metabolism (BGluM) was measured using positron emission tomography (μPET). RESULTS:Bacon CPP was only observed in RYGB rats that had stable weight loss following surgery. BGluM assessment revealed that RYGB selectively activated regions of the right and midline cerebellum (Lob 8) involved in subjective processes related to reward or expectation. Also, bacon anticipation led to significant activation in the medial parabrachial nuclei (important in gustatory processing) and dorsomedial tegmental area (key to reward, motivation, cognition and addiction) in RYGB rats; and activation in the retrosplenial cortex (default mode network), and the primary visual cortex in control rats. CONCLUSIONS:RYGB alters brain activity in areas involved in reward expectation and sensory (taste) processing when anticipating a palatable fatty food. Thus, RYGB may lead to changes in brain activity in regions that process reward and taste-related behaviors. Specific cerebellar regions with altered metabolism following RYGB may help identify novel therapeutic targets for treatment of obesity

Weekly food and water intake during bacon- and chow-conditioning.

<p>Weekly food <b>(A)</b> and water <b>(B)</b> intakes were calculated relative to CPP Weeks 1 and 2, and these values, expressed in calories and volume consumed, respectively, were adjusted based on body-weight (*unadjusted <i>p</i> < 0.001 versus Sham-AL,-PF and-ND; ^~unadjusted <i>p</i> < 0.001 versus Sham-PF and-AL; ^#unadjusted <i>p</i> < 0.007 versus all groups).</p

Healthy RYGB rats (Q1) showed significant bacon CPP.

<p>To quantify preference for the bacon-paired chamber, the amount of time each animal spent in the bacon-paired chamber was standardized to total time in both chambers. There was a bypass-specific increase in bacon-chamber preference after conditioning (Test Days 1 and 2) relative to Habituation Day, yet importantly, this behavioral response was strictly observed in the RYGB animals that responded well to the surgery (*unadjusted <i>p</i> < 0.05 compared to Habituation Day).</p