Predictability of large future changes in a competitive evolving population

The dynamical evolution of many economic, sociological, biological and physical systems tends to be dominated by a relatively small number of unexpected, large changes (`extreme events'). We study the large, internal changes produced in a generic multi-agent population competing for a limited resource, and find that the level of predictability actually increases prior to a large change. These large changes hence arise as a predictable consequence of information encoded in the system's global state.Comment: 10 pages, 3 figure

The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-know micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events

High frequency statistical arbitrage via the optimal thermal causal path

We consider the problem of identifying similarities and causality relationships in a given set of financial time series data streams. We develop further the “Optimal Thermal Causal Path” method, which is a non-parametric method proposed by Sornette et al. The method considers the mismatch between a given pair of time series in order to identify the expected minimum energy path lead-lag structure between the pair. Traders may find this a useful tool for directional trading, to spot arbitrage opportunities. We add a curvature energy term to the method and we propose an approximation technique to reduce the computational time. We apply the method and approximation technique on various market sectors of NYSE data and extract the highly correlated pairs of time series. We show how traders could exploit arbitrage opportunities by using the method

A Novel Animal Model of Borrelia recurrentis Louse-Borne Relapsing Fever Borreliosis Using Immunodeficient Mice

Louse-borne relapsing fever (LBRF) borreliosis is caused by Borrelia recurrentis, and it is a deadly although treatable disease that is endemic in the Horn of Africa but has epidemic potential. Research on LBRF has been severely hampered because successful infection with B. recurrentis has been achieved only in primates (i.e., not in other laboratory or domestic animals). Here, we present the first non-primate animal model of LBRF, using SCID (-B, -T cells) and SCID BEIGE (-B, -T, -NK cells) immunocompromised mice. These animals were infected with B. recurrentis A11 or A17, or with B. duttonii 1120K3 as controls. B. recurrentis caused a relatively mild but persistent infection in SCID and SCID BEIGE mice, but did not proliferate in NUDE (-T) and BALB/c (wild-type) mice. B. duttonii was infectious but not lethal in all animals. These findings demonstrate that the immune response can limit relapsing fever even in the absence of humoral defense mechanisms. To study the significance of phagocytic cells in this context, we induced systemic depletion of such cells in the experimental mice by injecting them with clodronate liposomes, which resulted in uncontrolled B. duttonii growth and a one-hundred-fold increase in B. recurrentis titers in blood. This observation highlights the role of macrophages and other phagocytes in controlling relapsing fever infection. B. recurrentis evolved from B. duttonii to become a primate-specific pathogen that has lost the ability to infect immunocompetent rodents, probably through genetic degeneration. Here, we describe a novel animal model of B. recurrentis based on B- and T-cell-deficient mice, which we believe will be very valuable in future research on LBRF. Our study also reveals the importance of B-cells and phagocytes in controlling relapsing fever infection

Enriched Population of PNS Neurons Derived from Human Embryonic Stem Cells as a Platform for Studying Peripheral Neuropathies

BACKGROUND: The absence of a suitable cellular model is a major obstacle for the study of peripheral neuropathies. Human embryonic stem cells hold the potential to be differentiated into peripheral neurons which makes them a suitable candidate for this purpose. However, so far the potential of hESC to differentiate into derivatives of the peripheral nervous system (PNS) was not investigated enough and in particular, the few trials conducted resulted in low yields of PNS neurons. Here we describe a novel hESC differentiation method to produce enriched populations of PNS mature neurons. By plating 8 weeks hESC derived neural progenitors (hESC-NPs) on laminin for two weeks in a defined medium, we demonstrate that over 70% of the resulting neurons express PNS markers and 30% of these cells are sensory neurons. METHODS/FINDINGS: Our method shows that the hNPs express neuronal crest lineage markers in a temporal manner, and by plating 8 weeks hESC-NPs into laminin coated dishes these hNPs were promoted to differentiate and give rise to homogeneous PNS neuronal populations, expressing several PNS lineage-specific markers. Importantly, these cultures produced functional neurons with electrophysiological activities typical of mature neurons. Moreover, supporting this physiological capacity implantation of 8 weeks old hESC-NPs into the neural tube of chick embryos also produced human neurons expressing specific PNS markers in vivo in just a few days. Having the enriched PNS differentiation system in hand, we show for the first time in human PNS neurons the expression of IKAP/hELP1 protein, where a splicing mutation on the gene encoding this protein causes the peripheral neuropathy Familial Dysautonomia. CONCLUSIONS/SIGNIFICANCE: We conclude that this differentiation system to produce high numbers of human PNS neurons will be useful for studying PNS related neuropathies and for developing future drug screening applications for these diseases

Single-molecule techniques in biophysics : a review of the progress in methods and applications

Single-molecule biophysics has transformed our understanding of the fundamental molecular processes involved in living biological systems, but also of the fascinating physics of life. Far more exotic than a collection of exemplars of soft matter behaviour, active biological matter lives far from thermal equilibrium, and typically covers multiple length scales from the nanometre level of single molecules up several orders of magnitude to longer length scales in emergent structures of cells, tissues and organisms. Biological molecules are often characterized by an underlying instability, in that multiple metastable free energy states exist which are separated by energy levels of typically just a few multiples of the thermal energy scale of kBT, where kB is the Boltzmann constant and T the absolute temperature, implying complex, dynamic inter-conversion kinetics across this bumpy free energy landscape in the relatively hot, wet environment of real, living biological matter. The key utility of single-molecule biophysics lies in its ability to probe the underlying heterogeneity of free energy states across a population of molecules, which in general is too challenging for conventional ensemble level approaches which measure mean average properties. Parallel developments in both experimental and theoretical techniques have been key to the latest insights and are enabling the development of highly-multiplexed, correlative techniques to tackle previously intractable biological problems. Experimentally, technological developments in the sensitivity and speed of biomolecular detectors, the stability and efficiency of light sources, probes and microfluidics, have enabled and driven the study of heterogeneous behaviours both in vitro and in vivo that were previously undetectable by ensemble methods..