Nonlinear Correlograms and Partial Autocorrelograms

This paper proposes neural network based measures of predictability in conditional mean, and then uses them to construct nonlinear analogues to autocorrelograms and partial autocorrelograms. In contrast to other measures of nonlinear dependence that rely on nonparametric estimation of densities or multivariate integration, our autocorrelograms are simple to calculate and appear to work well in relatively small samples.Nonlinear autocorrelograms, Nonlinear time series models, Neural networks, Model selection criteria, Nonlinear partial autocorrelograms

Theory of Nucleosome Corkscrew Sliding in the Presence of Synthetic DNA Ligands

Histone octamers show a heat-induced mobility along DNA. Recent theoretical studies have established two mechanisms that are qualitatively and quantitatively compatible with in vitro experiments on nucleosome sliding: Octamer repositiong through one-basepair twist defects and through ten-basepair bulge defects. A recent experiment demonstrated that the repositioning is strongly suppressed in the presence of minor-groove binding DNA ligands. In the present study we give a quantitative theory for nucleosome repositioning in the presence of such ligands. We show that the experimentally observed octamer mobilities are consistent with the picture of bound ligands blocking the passage of twist defects through the nucleosome. This strongly supports the model of twist defects inducing a corkscrew motion of the nucleosome as the underlying mechanism of nucleosome sliding. We provide a theoretical estimate of the nucleosomal mobility without adjustable parameters, as a function of ligand concentration, binding affinity, binding site orientiation, temperature and DNA anisotropy. Having this mobility at hand we speculate about the interaction between a nucleosome and a transcribing RNA polymerase and suggest a novel mechanism that might account for polymerase induced nucleosome repositioning.Comment: 23 pages, 4 figures, submitted to J. Mol. Bio

Do Jumps Matter? Forecasting Multivariate Realized Volatility allowing for Common Jumps

Realized volatility of stock returns is often decomposed into two distinct components that are attributed to continuous price variation and jumps. This paper proposes a tobit multivariate factor model for the jumps coupled with a standard multivariate factor model for the continuous sample path to jointly forecast volatility in three Chinese Mainland stocks. Out of sample forecast analysis shows that separate multivariate factor models for the two volatility processes outperform a single multivariate factor model of realized volatility, and that a single multivariate factor model of realized volatility outperforms univariate models.Realized Volatility, Bipower Variation, Jumps, Common Factors, Forecasting

Nonlinear Autoregresssive Leading Indicator Models of Output in G-7 Countries

This paper studies linear and nonlinear autoregressive leading indicator models of business cycles in G7 countries. The models use the spread between short-term and long-term interest rates as leading indicators for GDP, and their success in capturing business cycles is gauged by non-parametric shape tests, and their ability to predict the probability of recession. We find that bivariate nonlinear models of output and the interest rate spread can successfully capture the shape of the business cycle in cases where linear models fail. Also, our nonlinear leading indicator models for USA, Canada and the UK outperform other models of GDP with respect to predicting the probability of recession.Business Cycles, Leading Indicators, Model Evaluation, Nonlinear Models, Yield Spreads.

Forecasting the volatility of Australian stock returns: do common factors help?

This paper develops univariate and multivariate forecasting models for realized volatility in Australian stocks. We consider multivariate models with common features or common factors, and we suggest estimation procedures for approximate factor models that are robust to jumps when the cross-sectional dimension is not very large. Our forecast analysis shows that multivariate models outperform univariate models, but that there is little difference between simple and sophisticated factor models

Genome engineering of stem cells for autonomously regulated, closed-loop delivery of biologic drugs

Chronic inflammatory diseases such as arthritis are characterized by dysregulated responses to pro-inflammatory cytokines such as interleukin-1 (IL-1) and tumor necrosis factor α (TNF-α). Pharmacologic anti-cytokine therapies are often effective at diminishing this inflammatory response but have significant side effects and are used at high, constant doses that do not reflect the dynamic nature of disease activity. Using the CRISPR/Cas9 genome-engineering system, we created stem cells that antagonize IL-1- or TNF-α-mediated inflammation in an autoregulated, feedback-controlled manner. Our results show that genome engineering can be used successfully to rewire endogenous cell circuits to allow for prescribed input/output relationships between inflammatory mediators and their antagonists, providing a foundation for cell-based drug delivery or cell-based vaccines via a rapidly responsive, autoregulated system. The customization of intrinsic cellular signaling pathways in stem cells, as demonstrated here, opens innovative possibilities for safer and more effective therapeutic approaches for a wide variety of diseases

Training modalities in robot-mediated upper limb rehabilitation in stroke : A framework for classification based on a systematic review

© 2014 Basteris et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The work described in this manuscript was partially funded by the European project ‘SCRIPT’ Grant agreement no: 288698 (http://scriptproject.eu). SN has been hosted at University of Hertfordshire in a short-term scientific mission funded by the COST Action TD1006 European Network on Robotics for NeuroRehabilitationRobot-mediated post-stroke therapy for the upper-extremity dates back to the 1990s. Since then, a number of robotic devices have become commercially available. There is clear evidence that robotic interventions improve upper limb motor scores and strength, but these improvements are often not transferred to performance of activities of daily living. We wish to better understand why. Our systematic review of 74 papers focuses on the targeted stage of recovery, the part of the limb trained, the different modalities used, and the effectiveness of each. The review shows that most of the studies so far focus on training of the proximal arm for chronic stroke patients. About the training modalities, studies typically refer to active, active-assisted and passive interaction. Robot-therapy in active assisted mode was associated with consistent improvements in arm function. More specifically, the use of HRI features stressing active contribution by the patient, such as EMG-modulated forces or a pushing force in combination with spring-damper guidance, may be beneficial.Our work also highlights that current literature frequently lacks information regarding the mechanism about the physical human-robot interaction (HRI). It is often unclear how the different modalities are implemented by different research groups (using different robots and platforms). In order to have a better and more reliable evidence of usefulness for these technologies, it is recommended that the HRI is better described and documented so that work of various teams can be considered in the same group and categories, allowing to infer for more suitable approaches. We propose a framework for categorisation of HRI modalities and features that will allow comparing their therapeutic benefits.Peer reviewedFinal Published versio

Feasibility of a second iteration wrist and hand supported training system for self-administered training at home in chronic stroke

Telerehabilitation allows continued rehabilitation at home after discharge. The use of rehabilitation technology supporting wrist and hand movements within a motivational gaming environment could enable patients to train independently and ultimately serve as a way to increase the dosage of practice. This has been previously examined in the European SCRIPT project using a first prototype, showing potential feasibility, although several usability issues needed further attention. The current study examined feasibility and clinical changes of a second iteration training system, involving an updated wrist and hand supporting orthosis and larger variety of games with respect to the first iteration. Nine chronic stroke patients with impaired arm and hand function were recruited to use the training system at home for six weeks. Evaluation of feasibility and arm and hand function were assessed before and after training. Median weekly training duration was 113 minutes. Participants accepted the six weeks of training (median Intrinsic Motivation Inventory = 4.4 points and median System Usability Scale = 73%). After training, significant improvements were found for the Fugl Meyer assessment, Action Research Arm Test and self-perceived amount of arm and hand use in daily life. These findings indicate that technology-supported arm and hand training can be a promising tool for self-administered practice at home after stroke.Final Accepted Versio