Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcriptional activator C protein-mediated unwinding of DNA as a possible mechanism for mom gene activation.

The bacteriophage Mu mom gene encodes the unique DNA-modification function of the phage. Regulation of the mom gene at the transcriptional level is brought about by the transactivator protein C of the phage. The mom promoter is an activator-dependent weak promoter having poor -10 and -35 elements separated by a 19 bp suboptimal spacer region. These features could constrain RNA polymerase occupancy at the promoter. Here, we have probed into the mechanism by which C protein acts as a transcriptional activator at Pmom. In vivo dimethyl sulfate footprinting studies demonstrate C protein-mediated asymmetric distortion of its specific site at the mom regulatory region. Using a coupled topoisomerase assay, we demonstrate that C protein induces the unwinding of DNA. This C-mediated unwinding seems to be localised to the 3' flanking region of the C binding site located adjacent to and overlapping the -35 element of Pmom. These results suggest that C protein-mediated torsional changes could be reorienting the -10 and -35 elements to a favorable conformation for RNA polymerase occupancy at the mom promoter.
Insider trading, traded volume and returns

Several models predict that both market liquidity and trading volume generated by less informed traders do not increase when there is insider trading. Available empirical evidence is mixed and still relatively small, because of the inherent di¢ culty to identify insider trading events. Our econometric work, based on 19 suspect insider trading events drawn from the non-public ? file of the Italian supervisory authority, provides further insight on these key implications of stock market models. The second purpose of this paper is to assess whether insider trading changes the distribution of volume and returns in a way that can be used by supervisory authorities in order to detect its presence through statistical methods.
Market Time DataTM Improving Technical Analysis and Technical Trading

The purpose of this paper is to demonstrate that by changing the underlying data used in technical analysis and technical trading systems the performance of these techniques can be greatly improved. We present two techniques – one for real-time (intraday) data and one for standard daily (end-of-day) data. For high frequency data we present a dynamic sampling technique that can generate a time series that ranges from 5 minutes to many hours in our time scale, Market Time. We present empirical evidence that this modified series is superior for technical analysis and trading. This is based on correlation studies, directional forecasts, and profitability of trading rules for FX rates. In the daily data realm a different technique is used then for intraday data, but it is based on the same first principles. Using the FxDx currency index as a test vehicle, the new daily data series is evaluated and shows increased performance and reduced drawdown. The average increase in performance is greater than 1% per year over the FxDx benchmark. Finally, two other trend following systems are presented that show performance increases when using Daily Market Time DataTM.
Short Paper: 'A peer to peer security protocol for the internet of things': Secure communication for the sensiblethings platform

Nowadays, the Internet of Things is able to provide more advanced services to people, which connects a variety of devices, systems and applications. However, it covers a variety of devices, protocols and applications, which makes it much more complex. In the main application areas, wireless sensor networks with a frequent but short communication character, it requires an efficient and flexible protocol to protect the information. To protect the traffic of the Internet of Things is the focal point of this research work. Although many protocols for the Internet have been put forward, it is still not enough to meet the increasingly complex requirements from applications. Many of them are not efficient enough to adapt the device diversity and timely communication environment. This paper is trying to address this problem, by proposing a peer-to-peer security protocol to satisfy this varied environment. Secure communication is implemented on an open sourced platform for the Internet of Things. Its less handshake in future sessions and dynamic security level adjustment feature satisfies the realistic demand on this platform. Finally, with a comparison test, the result shows that the proposed protocol is efficient to meet the specific goals and applicable for the Internet of Things applications.
Income and Preventable Mortality: The Case of Youth Traffic Fatalities

The income-health gradient is a well-established finding in public health. This paper explores the gradient between income and different types of mortality: mortality that can be ameliorated via specific public policy measures, namely traffic fatalities, and mortality that is due to more “natural” causes, such as infectious disease. Using U.S. state-level data, growth in traffic mortality for 15-19 year-olds is shown to be more sensitive to initial levels of median income than growth in non-injury mortality. In addition, some but not all traffic safety legislation aimed at this age group is shown to be associated with lower mortality. Results are established via cross-section estimates, panel-data type models, and tests of one-step-ahead prediction
Sentiment analysis with limited training data

Sentiments are positive and negative emotions, evaluations and stances. This dissertation focuses on learning based systems for automatic analysis of sentiments and comparisons in natural language text. The proposed approach consists of three contributions: 1. Bag-of-opinions model: For predicting document-level polarity and intensity, we proposed the bag-of-opinions model by modeling each document as a bag of sentiments, which can explore the syntactic structures of sentiment-bearing phrases for improved rating prediction of online reviews. 2. Multi-experts model: Due to the sparsity of manually-labeled training data, we designed the multi-experts model for sentence-level analysis of sentiment polarity and intensity by fully exploiting any available sentiment indicators, such as phrase-level predictors and sentence similarity measures. 3. SENTI-LSSVMRAE model: To understand the sentiments regarding entities, we proposed SENTI-LSSVMRAE model for extracting sentiments and comparisons of entities at both sentence and subsentential level. Different granularity of analysis leads to different model complexity, the finer the more complex. All proposed models aim to minimize the use of hand-labeled data by maximizing the use of the freely available resources. These models explore also different feature representations to capture the compositional semantics inherent in sentiment-bearing expressions. Our experimental results on real-world data showed that all models significantly outperform the state-of-the-art methods on the respective tasks.
Spatial Firing Correlates of Physiologically Distinct Cell Types of the Rat Dentate Gyrus

The dentate gyrus (DG) occupies a key position in information flow through the hippocampus. Its principal cell, the granule cell, has spatially selective place fields. However, the behavioral correlates of cells located in the hilus of the rat dentate gyrus are unknown. We report here that cells below the granule layer show spatially selective firing that consists of multiple subfields. Other cells recorded from the DG had single place fields. Compared with cells with multiple fields, cells with single fields fired at lower rates during sleep were less bursty, and were more likely to be recorded simultaneously with large populations of neurons that were active during sleep and silent during behavior. We propose that cells with single fields are likely to be mature granule cells that use sparse encoding to potentially disambiguate input patterns. Furthermore, we hypothesize that cells with multiple fields might be cells of the hilus or newborn granule cells. These data are the first demonstration, based on physiological criteria, that single- and multiple-field cells constitute at least two distinct cell classes in the DG. Because of the heterogeneity of firing correlates and cell types in the DG, understanding which cell types correspond to which firing patterns, and how these correlates change with behavioral state and between different environments, are critical questions for testing long-standing computational theories that the DG performs a pattern separation function using a very sparse coding strategy.
Neomycin resistance as a dominant selectable marker for selection and isolation of vaccinia virus recombinants

The antibiotic G418 was shown to be an effective inhibitor of vaccinia virus replication when an appropriate concentration of it was added to cell monolayers 48 h before infection. Genetic engineering techniques were used in concert with DNA transfection protocols to construct vaccinia virus recombinants containing the neomycin resistance gene (neo) from transposon Tn5. These recombinants contained the neo gene linked in either the correct or incorrect orientation relative to the vaccinia virus 7.5-kilodalton gene promoter which is expressed constitutively throughout the course of infection. The vaccinia virus recombinant containing the chimeric neo gene in the proper orientation was able to grow and form plaques in the presence of G418, whereas both the wild-type and the recombinant virus with the neo gene in the opposite polarity were inhibited by more than 98%. The effect of G418 on virus growth may be mediated at least in part by selective inhibition of the synthesis of a subset of late viral proteins. These results are discussed with reference to using this system, the conferral of resistance to G418 with neo as a positive selectable marker, to facilitate constructing vaccinia virus recombinants which contain foreign genes of interest.
Incremental Tree-Based Inference with Dependent Normalized Random Measures

Normalized random measures (NRMs) form a broad class of discrete random measures that are used as priors for Bayesian nonparametric models. Dependent normalized random measures (DNRMs) introduce dependencies in a set of NRMs, to facilitate the handling of data where the assumption of exchangeability is violated. Various methods have been developed to construct DNRMs; of particular interest is mixed normalized random measures (MNRMs), where DNRM is represented as a mixture of underlying shared normalized random measures. Emphasis in existing works is placed on the construction methods of DNRMs, but there is a little work on efficient inference for DNRMs. In this paper, we present a tree-based inference method for MNRM mixture models, extending Bayesian hierarchical clustering (BHC) which was originally developed as a deterministic approximate inference for Dirichlet process mixture (DPM) models. We also present an incremental inference for MNRM mixture models, building a tree incrementally in the sense that the tree structure is partially updated whenever a new data point comes in. The tree, when constructed in such a way, allows us to efficiently perform tree-consistent MAP inference in MRNM mixture models, determining a most probable tree-consistent partition, as well as to compute a marginal likelihood approximately. Numerical experiments on both synthetic and real-world datasets demonstrate the usefulness of our algorithm, compared to MCMC methods.
Comparative study of generating referring expressions in situated dialogues

This paper focuses on generating referring expressions (REs) using the REX corpora, which con- sist of the set of dialogues where two participants collaboratively solve Tangram puzzles. In this study, we show our manual analysis of the different use of the REs in English and Japanese corpora. In addition, we report the results of our empirical evaluation replicating the usage of demonstrative pronouns in the corpora by machine learning techniques.
Time-Lapse Analysis and Mathematical Characterization Elucidate Novel Mechanisms Underlying Muscle Morphogenesis

Skeletal muscle morphogenesis transforms short muscle precursor cells into long, multinucleate myotubes that anchor to tendons via the myotendinous junction (MTJ). In vertebrates, a great deal is known about muscle specification as well as how somitic cells, as a cohort, generate the early myotome. However, the cellular mechanisms that generate long muscle fibers from short cells and the molecular factors that limit elongation are unknown. We show that zebrafish fast muscle fiber morphogenesis consists of three discrete phases: short precursor cells, intercalation/elongation, and boundary capture/myotube formation. In the first phase, cells exhibit randomly directed protrusive activity. The second phase, intercalation/elongation, proceeds via a two-step process: protrusion extension and filling. This repetition of protrusion extension and filling continues until both the anterior and posterior ends of the muscle fiber reach the MTJ. Finally, both ends of the muscle fiber anchor to the MTJ (boundary capture) and undergo further morphogenetic changes as they adopt the stereotypical, cylindrical shape of myotubes. We find that the basement membrane protein laminin is required for efficient elongation, proper fiber orientation, and boundary capture. These early muscle defects in the absence of either lamininβ1 or lamininγ1 contrast with later dystrophic phenotypes in lamininα2 mutant embryos, indicating discrete roles for different laminin chains during early muscle development. Surprisingly, genetic mosaic analysis suggests that boundary capture is a cell-autonomous phenomenon. Taken together, our results define three phases of muscle fiber morphogenesis and show that the critical second phase of elongation proceeds by a repetitive process of protrusion extension and protrusion filling. Furthermore, we show that laminin is a novel and critical molecular cue mediating fiber orientation and limiting muscle cell length.
The constitution of economic growth: Testing the prosperity effects of a Madisonian model on a panel of countries 1980‐2000

Political scientists and economists increasingly agree that institutions may influence economic growth, but there is little general agreement on what institutions tend to produce what consequences. We apply public choice insights for a theoretical analysis that may be termed “Madisonian”: Institutions that divide political power between multiple veto players and institutions that protect private property rights may be expected to have positive effects on economic growth. We analyze data from a panel of countries for the period 1980‐2000 in order to study the relationships, including a series of “extreme bounds” analyses in order to test the robustness of the statistical results. We find that particularly the presence of secure private property has a significant, positive and robust effect on economic growth and that when outliers are excluded a configuration where political power is dispersed among more veto players has a similar effect.
Linguistic and pragma-rhetorical characteristics of argumentative discourse in L2 and L1

(1) Unificazione europea: perdita dell’identità nazionale? Non penso che l’unificazione di Europa sarà una perdità dell’identità nazionale (Opinion 1). Secondo me Europa è formato di molto paesi che hanno tutto i propri valori e le proprie tradizione. Questi valori e tradizione sono formati per molti secoli e non camberanno di colpo (Argument 1). Certo, ci sarà uno scambio dei valori e delle tradizione tra i paesi (Counterargument 1 rejected) ma penso che questo scambio sarà utile per arricchere la propria cultura (Argument 2). Ci sono anche le lingue che sono molto diverse (Argument 3). L’inglese sarà la lingua principale (Counterargument 2 rejected), ma penso che non sia possibile (Qualifier 1) di trasformare tutte le lingue nell’inglese (Argument 4). Per me l’unificazione vuol dire che Europa sia un insieme di paesi separati con un zielo uguale: la collaborazione sul campo economico e politico (Conclusion 1).
Human Body Volume Estimation in a Clinical Environment

Medical scientists try to improve the accuracy of human body composition measurement by segmental Bioelectrical Impedance Analysis (BIA). Determination of body composition includes estimation of total body water, fat-free mass and extraand intra-cellular water, which are of great importance in disease states like cancer, malnutrition, disturbances of hydration as seen in heart-, hepaticand renal failure. Currently, body composition is derived by measuring the specific resistivity of the total body or of its segments and heuristically determining their volumes. Whereas the resistivity measurement is very accurate, the main uncertainty factor remains the volume estimation, which is performed through empirical models. To improve the accuracy of volume estimation a vision based measurement system is proposed. A portable system consisting of stereo cameras and projectors has been constructed, which can be used in a clinical environment even for bedridden, disabled and intensive care patients. In this work the main focus lies on realistic human body modeling and volume calculation from noisy three-dimensional data. The accuracy of the volume measurement has been compared to groundtruth data of two volunteers.
Have We Seen Enough Traces? (T)

Dynamic specification mining extracts candidate specifications from logs of execution traces. Existing algorithms differ in the kinds of traces they take as input and in the kinds of candidate specification they present as output. One challenge common to all approaches relates to the faithfulness of the mining results: how can we be confident that the extracted specifications faithfully characterize the program we investigate? Since producing and analyzing traces is costly, how would we know we have seen enough traces? And, how would we know we have not wasted resources and seen too many of them? In this paper we address these important questions by presenting a novel, black box, probabilistic framework based on a notion of log completeness, and by applying it to three different well-known specification mining algorithms from the literature: k-Tails, Synoptic, and mining of scenario-based triggers and effects. Extensive evaluation over 24 models taken from 9 different sources shows the soundness, generalizability, and usefulness of the framework and its contribution to the state-of-the-art in dynamic specification mining.
A framework for parallel genetic algorithms on PC cluster

Genetic algorithms (GAs) is a kind of powerful method for solving complex problems. Generally, parallel computing is used together with GAs to achieve better execution time and there exist several models for Parallel Genetic Algorithms (PGAs), such as master-salve model and island model. Currently, PC cluster is the common architecture for parallel computing with high performance-price-ratio. In this paper, we choose island model for implement PGA on PC cluster and present a novel framework for PGAs (FPGA-Cluster) using updated island model on PC cluster. A GA program can be translated to a PGA program on PC cluster by FPGA-Cluster easily and efficiently, and an application for FPGA-Cluster is also demonstrated.
Automated Web Service Composition Using Extended Representation of Planning Domain

WS are distributed software components that can be exposed and invoked over the Internet using standard protocols. They communicate with their clients and with other WS by sending XML based messages over the Internet. AI planning techniques can help solving the composition of WS problem. In fact, services can be modelled as actions and the business process as planning to connect the WS. The main contribution of this paper is the extension of the model of actions to handle the creation or elimination of objects as effects of actions. This contribution allows us to answer to new and more expressive requests, called implicit requests, in which goals may contain objects that have been generated by the plan.
Benoît Kornmann , by a Synthetic Biology Screen An ER-Mitochondria Tethering Complex Revealed

www.sciencemag. org (this information is current as of August 19, 2009 ): The following resources related to this article are available online at http://www.sciencemag. org/cgi/content/full/325/5939/477 version of this article at: including high-resolution figures, can be found in the online Updated information and services, http://www.sciencemag. org/cgi/content/full/1175088/DC1 can be found at: Supporting Online Material found at: can be related to this article A list of selected additional articles on the Science Web sites http://www.sciencemag. org/cgi/content/full/325/5939/477#related-content http://www.sciencemag. org/cgi/content/full/325/5939/477#otherarticles , 19 of which can be accessed for free: cites 32 articles This article http://www.sciencemag. org/cgi/content/full/325/5939/477#otherarticles 1 articles hosted by HighWire Press; see: cited by This article has been http://www.sciencemag. org/cgi/collection/cell_biol Cell Biology : subject collections This article appears in the following http://www.sciencemag. org/about/permissions.dtl in whole or in part can be found at: this article permission to reproduce of this article or about obtaining reprints Information about obtaining
Coordination of Hepatitis C Virus Assembly by Distinct Regulatory Regions in Nonstructural Protein 5A

Hepatitis C virus (HCV) nonstructural protein (NS)5A is a RNA-binding protein composed of a N-terminal membrane anchor, a structured domain I (DI) and two intrinsically disordered domains (DII and DIII) interacting with viral and cellular proteins. While DI and DII are essential for RNA replication, DIII is required for assembly. How these processes are orchestrated by NS5A is poorly understood. In this study, we identified a highly conserved basic cluster (BC) at the N-terminus of DIII that is critical for particle assembly. We generated BC mutants and compared them with mutants that are blocked at different stages of the assembly process: a NS5A serine cluster (SC) mutant blocked in NS5A-core interaction and a mutant lacking the envelope glycoproteins (ΔE1E2). We found that BC mutations did not affect core-NS5A interaction, but strongly impaired core–RNA association as well as virus particle envelopment. Moreover, BC mutations impaired RNA-NS5A interaction arguing that the BC might be required for loading of core protein with viral RNA. Interestingly, RNA-core interaction was also reduced with the ΔE1E2 mutant, suggesting that nucleocapsid formation and envelopment are coupled. These findings argue for two NS5A DIII determinants regulating assembly at distinct, but closely linked steps: (i) SC-dependent recruitment of replication complexes to core protein and (ii) BC-dependent RNA genome delivery to core protein, triggering encapsidation that is tightly coupled to particle envelopment. These results provide a striking example how a single viral protein exerts multiple functions to coordinate the steps from RNA replication to the assembly of infectious virus particles.