Ran GTPase in Nuclear Envelope Formation and Cancer Metastasis

Ran is a small ras-related GTPase that controls the nucleocytoplasmic exchange of macromolecules across the nuclear envelope. It binds to chromatin early during nuclear formation and has important roles during the eukaryotic cell cycle, where it regulates mitotic spindle assembly, nuclear envelope formation and cell cycle checkpoint control. Like other GTPases, Ran relies on the cycling between GTP-bound and GDP-bound conformations to interact with effector proteins and regulate these processes. In nucleocytoplasmic transport, Ran shuttles across the nuclear envelope through nuclear pores. It is concentrated in the nucleus by an active import mechanism where it generates a high concentration of RanGTP by nucleotide exchange. It controls the assembly and disassembly of a range of complexes that are formed between Ran-binding proteins and cellular cargo to maintain rapid nuclear transport. Ran also has been identified as an essential protein in nuclear envelope formation in eukaryotes. This mechanism is dependent on importin-β, which regulates the assembly of further complexes important in this process, such as Nup107–Nup160. A strong body of evidence is emerging implicating Ran as a key protein in the metastatic progression of cancer. Ran is overexpressed in a range of tumors, such as breast and renal, and these perturbed levels are associated with local invasion, metastasis and reduced patient survival. Furthermore, tumors with oncogenic KRAS or PIK3CA mutations are addicted to Ran expression, which yields exciting future therapeutic opportunities

Artificial receptors for the extraction of nucleoside metabolite 7-methylguanosine from aqueous media made by molecular imprinting

A series of imprinted polymers targeting nucleoside metabolites, prepared using a template analogue approach, are presented. These were prepared following selection of the optimum functional monomer by solution association studies using 1H-NMR titrations whereby methacrylic acid was shown to be the strongest receptor with and affinity constant of 621 ± 51 L mol-1 vs. 110 ± 16 L mol-1 for acrylamide. The best performing polymers were prepared using methanol as porogenic co-solvent and although average binding site affinities were marginally reduced, 2.3×104 L mol-1 vs. 2.7×104 L mol-1 measured for a polymer prepared in acetonitrile, these polymers contained the highest number of binding sites, 5.27 μmol g-1¬¬ vs. 1.64 μmol g-1, while they also exhibited enhanced selectivity for methylated guanosine derivatives. When applied as sorbents in the extraction of nucleoside derivative cancer biomarkers from synthetic urine samples, significant sample clean-up and recoveries of up to 90% for 7-methylguanosine were achieved.

Learning from time series:Supervised Aggregative Feature Extraction

Many modeling problems require to estimate a scalar output from one or more time series. Such problems are usually tackled by extracting a fixed number of features from the time series (like their statistical moments), with a consequent loss in information that leads to suboptimal predictive models. Moreover, feature extraction techniques usually make assumptions that are not met by real world settings (e.g. uniformly sampled time series of constant length), and fail to deliver a thorough methodology to deal with noisy data. In this paper a methodology based on functional learning is proposed to overcome the aforementioned problems; the proposed Supervised Aggregative Feature Extraction (SAFE) approach allows to derive continuous, smooth estimates of time series data (yielding aggregate local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The SAFE paradigm enjoys several properties like closed form solution, incorporation of first and second order derivative information into the regressor matrix, interpretability of the generated functional predictor and the possibility to exploit Reproducing Kernel Hilbert Spaces setting to yield nonlinear predictive models. Simulation studies are provided to highlight the strengths of the new methodology w.r.t. standard unsupervised feature selection approaches. © 2012 IEEE.

Use of water in aiding olefin/paraffin (liquid + liquid) extraction via complexation with a silver bis(trifluoromethylsulfonyl)imide salt

This paper describes the extraction of C₅–C₈ linear α-olefins from olefin/paraffin mixtures of the same carbon number via a reversible complexation with a silver salt (silver bis(trifluoromethylsulfonyl)imide, Ag[Tf₂N]) to form room temperature ionic liquids [Ag(olefin)_x][Tf₂N]. From the experimental (liquid + liquid) equilibrium data for the olefin/paraffin mixtures and Ag[Tf₂N], 1-pentene showed the best separation performance while C₇ and C₈ olefins could only be separated from the corresponding mixtures on addition of water which also improves the selectivity at lower carbon numbers like the C₅ and C₆, for example. Using infrared and Raman spectroscopy of the complex and Ag[Tf₂N] saturated by olefin, the mechanism of the extraction was found to be based on both chemical complexation and the physical solubility of the olefin in the ionic liquid ([Ag(olefin)_x][Tf₂N]). These experiments further support the use of such extraction techniques for the separation of olefins from paraffins.

Insight Extraction for Semiconductor Manufacturing Processes

In the semiconductor manufacturing environment it is very important to understand which factors have the most impact on process outcomes and to control them accordingly. This is usually achieved through design of experiments at process start-up and long term observation of production. As such it relies heavily on the expertise of the process engineer. In this work, we present an automatic approach to extracting useful insights about production processes and equipment based on state-of-the-art Machine Learning techniques. The main goal of this activity is to provide tools to process engineers to accelerate the learning-by-observation phase of process analysis. Using a Metal Deposition process as an example, we highlight various ways in which the extracted information can be employed.

Automatic extraction of Reduced order models from CFD simulations for building energy modeling

Thermal comfort is defined as “that condition of mind which expresses satisfaction with the thermal environment’ [1] [2]. Field studies have been completed in order to establish the governing conditions for thermal comfort [3]. These studies showed that the internal climate of a room was the strongest factor in establishing thermal comfort. Direct manipulation of the internal climate is necessary to retain an acceptable level of thermal comfort. In order for Building Energy Management Systems (BEMS) strategies to be efficiently utilised it is necessary to have the ability to predict the effect that activating a heating/cooling source (radiators, windows and doors) will have on the room. The numerical modelling of the domain can be challenging due to necessity to capture temperature stratification and/or different heat sources (radiators, computers and human beings). Computational Fluid Dynamic (CFD) models are usually utilised for this function because they provide the level of details required. Although they provide the necessary level of accuracy these models tend to be highly computationally expensive especially when transient behaviour needs to be analysed. Consequently they cannot be integrated in BEMS. This paper presents and describes validation of a CFD-ROM method for real-time simulations of building thermal performance. The CFD-ROM method involves the automatic extraction and solution of reduced order models (ROMs) from validated CFD simulations. The test case used in this work is a room of the Environmental Research Institute (ERI) Building at the University College Cork (UCC). ROMs have shown that they are sufficiently accurate with a total error of less than 1% and successfully retain a satisfactory representation of the phenomena modelled. The number of zones in a ROM defines the size and complexity of that ROM. It has been observed that ROMs with a higher number of zones produce more accurate results. As each ROM has a time to solution of less than 20 seconds they can be integrated into the BEMS of a building which opens the potential to real time physics based building energy modelling.

Correlation approach to work extraction from finite quantum systems

Reversible work extraction from identical quantum systems via collective operations was shown to be possible even without producing entanglement among the sub-parts. Here, we show that implementing such global operations necessarily imply the creation of quantum correlations, as measured by quantum discord. We also reanalyze the conditions under which global transformations outperform local gates as far as maximal work extraction is considered by deriving a necessary and sufficient condition that is based on classical correlations.

Coherent spectral enhancement of carrier-envelope-phase stable continua with dual-gas high harmonic generation

Attosecond science is enabled by the ability to convert femtosecond near-infrared laser light into coherent harmonics in the extreme ultraviolet spectral range. While attosecond sources have been utilized in experiments that have not demanded high intensities, substantially higher photon flux would provide a natural link to the next significant experimental breakthrough. Numerical simulations of dual-gas high harmonic generation indicate that the output in the cutoff spectral region can be selectively enhanced without disturbing the single-atom gating mechanism. Here, we summarize the results of these simulations and present first experimental findings to support these predictions. (c) 2012 Optical Society of America

Evaluation of information extraction techniques to label extracted data from e-commerce web page

Automatically determining and assigning shared and meaningful text labels to data extracted from an e-Commerce web page is a challenging problem. An e-Commerce web page can display a list of data records, each of which can contain a combination of data items (e.g. product name and price) and explicit labels, which describe some of these data items. Recent advances in extraction techniques have made it much easier to precisely extract individual data items and labels from a web page, however, there are two open problems: 1. assigning an explicit label to a data item, and 2. determining labels for the remaining data items. Furthermore, improvements in the availability and coverage of vocabularies, especially in the context of e-Commerce web sites, means that we now have access to a bank of relevant, meaningful and shared labels which can be assigned to extracted data items. However, there is a need for a technique which will take as input a set of extracted data items and assign automatically to them the most relevant and meaningful labels from a shared vocabulary. We observe that the Information Extraction (IE) community has developed a great number of techniques which solve problems similar to our own. In this work-in-progress paper we propose our intention to theoretically and experimentally evaluate different IE techniques to ascertain which is most suitable to solve this problem.

Amplitude modulation of quantum-ion-acoustic wavepackets in electron-positron-ion plasmas: Modulational instability, envelope modes, extreme waves

A semirelativistic fluid model is employed to describe the nonlinear amplitude modulation of low-frequency (ionic scale) electrostatic waves in an unmagnetized electron-positron-ion plasma. Electrons and positrons are assumed to be degenerated and inertialess, whereas ions are warm and classical. A multiscale perturbation method is used to derive a nonlinear Schrödinger equation for the envelope amplitude, based on which the occurrence of modulational instability is investigated in detail. Various types of localized ion acoustic excitations are shown to exist, in the form of either bright type envelope solitons (envelope pulses) or dark-type envelope solitons (voids, holes). The plasma configurational parameters (namely, the relativistic degeneracy parameter, the positron concentration, and the ionic temperature) are shown to affect the conditions for modulational instability significantly, in fact modifying the associated threshold as well as the instability growth rate. In particular, the relativistic degeneracy parameter leads to an enhancement of the modulational instability mechanism. Furthermore, the effect of different relevant plasma parameters on the characteristics (amplitude, width) of these envelope solitary structures is also presented in detail. Finally, the occurrence of extreme amplitude excitation (rogue waves) is also discussed briefly. Our results aim at elucidating the formation and dynamics of nonlinear electrostatic excitations in superdense astrophysical regimes.

Automatic extraction of reduced-order models from CFD simulations for building energy modelling

Accurate modelling of the internal climate of buildings is essential if Building Energy Management Systems (BEMS) are to efficiently maintain adequate thermal comfort. Computational fluid dynamics (CFD) models are usually utilised to predict internal climate. Nevertheless CFD models, although providing the necessary level of accuracy, are highly computationally expensive, and cannot practically be integrated in BEMS. This paper presents and describes validation of a CFD-ROM method for real-time simulations of building thermal performance. The CFD-ROM method involves the automatic extraction and solution of reduced order models (ROMs) from validated CFD simulations. ROMs are shown to be adequately accurate with a total error below 5% and to retain satisfactory representation of the phenomena modelled. Each ROM has a time to solution under 20seconds, which opens the potential of their integration with BEMS, giving real-time physics-based building energy modelling. A parameter study was conducted to investigate the applicability of the extracted ROM to initial boundary conditions different from those from which it was extracted. The results show that the ROMs retained satisfactory total errors when the initial conditions in the room were varied by ±5°C. This allows the production of a finite number of ROMs with the ability to rapidly model many possible scenarios.