Proteogenomic analysis of Epibacterium mobile BBCC367, a relevant marine bacterium isolated from the South Pacific Ocean

Epibacterium mobile BBCC367 is a marine bacterium that is common in coastal areas. It belongs to the Roseobacter clade, a widespread group in pelagic marine ecosystems. Species of the Roseobacter clade are regularly used as models to understand the evolution and physiological adaptability of generalist bacteria. E. mobile BBCC367 comprises two chromosomes and two plasmids. We used gel-free shotgun proteomics to assess its protein expression under 16 different conditions, including stress factors such as elevated temperature, nutrient limitation, high metal concentration, and UVB exposure. Comparison of the different conditions allowed us not only to retrieve almost 70% of the predicted proteins, but also to define three main protein assemblages: 584 essential core proteins, 2,144 facultative accessory proteins and 355 specific unique proteins. While the core proteome mainly exhibited proteins involved in essential functions to sustain life such as DNA, amino acids, carbohydrates, cofactors, vitamins and lipids metabolisms, the accessory and unique proteomes revealed a more specific adaptation with the expression of stress-related proteins, such as DNA repair proteins (accessory proteome), transcription regulators and a significant predominance of transporters (unique proteome). Our study provides insights into how E. mobile BBCC367 adapts to environmental changes and copes with diverse stresses

Exploring Trading Strategies and Their Effects in the Foreign Exchange Market

One of the most critical issues that developers face in developing automatic systems for electronic markets is that of endowing the agents with appropriate trading strategies. In this article, we examine the problem in the foreign exchange (FX) market, and we use an agent‐based market simulation to examine which trading strategies lead to market states in which the stylized facts (statistical properties) of the simulation match those of the FX market transactions data. Our goal is to explore the emergence of the stylized facts, when the simulated market is populated with agents using different strategies: a variation of the zero intelligence with a constraint strategy, the zero‐intelligence directional‐change event strategy, and a genetic programming‐based strategy. A series of experiments were conducted, and the results were compared with those of a high‐frequency FX transaction data set. Our results show that the zero‐intelligence directional‐change event agents best reproduce and explain the properties observed in the FX market transactions data. Our study suggests that the observed stylized facts could be the result of introducing a threshold that triggers the agents to respond to periodic patterns in the price time series. The results can be used to develop decision support systems for the FX market

Why is order flow so persistent?

Order flow in equity markets is remarkably persistent in the sense that order signs (to buy or sell) are positively autocorrelated out to time lags of tens of thousands of orders, corresponding to many days. Two possible explanations are herding, corresponding to positive correlation in the behavior of different investors, or order splitting, corresponding to positive autocorrelation in the behavior of single investors. We investigate this using order flow data from the London Stock Exchange for which we have membership identifiers. By formulating models for herding and order splitting, as well as models for brokerage choice, we are able to overcome the distortion introduced by brokerage. On timescales of less than a few hours the persistence of order flow is overwhelmingly due to splitting rather than herding. We also study the properties of brokerage order flow and show that it is remarkably consistent both cross-sectionally and longitudinally.Comment: 42 pages, 15 figure

Herding effects in order driven markets: The rise and fall of gurus

We introduce an order driver market model with heterogeneous traders that imitate each other on a dynamic network structure. The communication structure evolves endogenously via a fitness mechanism based on agents performance. We assess under which assumptions imitation, among noise traders, can give rise to the emergence of gurus and their rise and fall in popularity over time. We study the wealth distribution of gurus, followers and non followers and show that traders have an incentive to imitate and a desire to be imitated since herding turns out to be profitable. The model is then used to study the effect that different competitive strategies (i.e. chartist & fundamentalist) have on agents performance. Our findings show that positive intelligence agents cannot invade a market populated by noise traders when herding is high

Market ecologies: The effect of information on the interaction and profitability of technical trading strategies

Technical trading strategies make profits by identifying and exploiting patterns in market prices—patterns generated by the interaction of market participants. Using a model market populated by individuals using a range of trading rules we show that the presence of technical traders may be beneficial, in some cases reducing volatility and increasing price efficiency. In particular, contrarian traders who base their decisions on high frequency data have the largest positive effect. It is also found that if technical traders condition their actions using ‘real time’ information, they partially emulate arbitrageurs and make positive profits

Behavioural breaks in the heterogeneous agent model: the impact of herding, overconfidence, and market sentiment

The main aim of this work is to incorporate selected findings from behavioural finance into a Heterogeneous Agent Model using the Brock and Hommes (1998) framework. Behavioural patterns are injected into an asset pricing framework through the so-called `Break Point Date', which allows us to examine their direct impact. In particular, we analyse the dynamics of the model around the behavioural break. Price behaviour of 30 Dow Jones Industrial Average constituents covering five particularly turbulent U.S. stock market periods reveals interesting pattern in this aspect. To replicate it, we apply numerical analysis using the Heterogeneous Agent Model extended with the selected findings from behavioural finance: herding, overconfidence, and market sentiment. We show that these behavioural breaks can be well modelled via the Heterogeneous Agent Model framework and they extend the original model considerably. Various modifications lead to significantly different results and model with behavioural breaks is also able to partially replicate price behaviour found in the data during turbulent stock market periods

Final Pre-40S Maturation Depends on the Functional Integrity of the 60S Subunit Ribosomal Protein L3

Ribosomal protein L3 is an evolutionarily conserved protein that participates in the assembly of early pre-60S particles. We report that the rpl3[W255C] allele, which affects the affinity and function of translation elongation factors, impairs cytoplasmic maturation of 20S pre-rRNA. This was not seen for other mutations in or depletion of L3 or other 60S ribosomal proteins. Surprisingly, pre-40S particles containing 20S pre-rRNA form translation-competent 80S ribosomes, and translation inhibition partially suppresses 20S pre-rRNA accumulation. The GTP-dependent translation initiation factor Fun12 (yeast eIF5B) shows similar in vivo binding to ribosomal particles from wild-type and rpl3[W255C] cells. However, the GTPase activity of eIF5B failed to stimulate processing of 20S pre-rRNA when assayed with ribosomal particles purified from rpl3[W255C] cells. We conclude that L3 plays an important role in the function of eIF5B in stimulating 3′ end processing of 18S rRNA in the context of 80S ribosomes that have not yet engaged in translation. These findings indicate that the correct conformation of the GTPase activation region is assessed in a quality control step during maturation of cytoplasmic pre-ribosomal particles