Dynamic single cell measurements of kinase activity by synthetic kinase activity relocation sensors.

BACKGROUND: Mitogen activated protein kinases (MAPK) play an essential role in integrating extra-cellular signals and intra-cellular cues to allow cells to grow, adapt to stresses, or undergo apoptosis. Budding yeast serves as a powerful system to understand the fundamental regulatory mechanisms that allow these pathways to combine multiple signals and deliver an appropriate response. To fully comprehend the variability and dynamics of these signaling cascades, dynamic and quantitative single cell measurements are required. Microscopy is an ideal technique to obtain these data; however, novel assays have to be developed to measure the activity of these cascades. RESULTS: We have generated fluorescent biosensors that allow the real-time measurement of kinase activity at the single cell level. Here, synthetic MAPK substrates were engineered to undergo nuclear-to-cytoplasmic relocation upon phosphorylation of a nuclear localization sequence. Combination of fluorescence microscopy and automated image analysis allows the quantification of the dynamics of kinase activity in hundreds of single cells. A large heterogeneity in the dynamics of MAPK activity between individual cells was measured. The variability in the mating pathway can be accounted for by differences in cell cycle stage, while, in the cell wall integrity pathway, the response to cell wall stress is independent of cell cycle stage. CONCLUSIONS: These synthetic kinase activity relocation sensors allow the quantification of kinase activity in live single cells. The modularity of the architecture of these reporters will allow their application in many other signaling cascades. These measurements will allow to uncover new dynamic behaviour that previously could not be observed in population level measurements.

Modelling body mass index and endometrial cancer risk in a pooled-analysis of three case-control studies.

OBJECTIVE: To quantify the relation between body mass index (BMI) and endometrial cancer risk, and to describe the shape of such a relation. DESIGN: Pooled analysis of three hospital-based case-control studies. SETTING: Italy and Switzerland. POPULATION: A total of 1449 women with endometrial cancer and 3811 controls. METHODS: Multivariate odds ratios (OR) and 95% confidence intervals (95% CI) were obtained from logistic regression models. The shape of the relation was determined using a class of flexible regression models. MAIN OUTCOME MEASURE: The relation of BMI with endometrial cancer. RESULTS: Compared with women with BMI 18.5 to <25 kg/m(2) , the odds ratio was 5.73 (95% CI 4.28-7.68) for women with a BMI ≥35 kg/m(2) . The odds ratios were 1.10 (95% CI 1.09-1.12) and 1.63 (95% CI 1.52-1.75) respectively for an increment of BMI of 1 and 5 units. The relation was stronger in never-users of oral contraceptives (OR 3.35, 95% CI 2.78-4.03, for BMI ≥30 versus <25 kg/m(2) ) than in users (OR 1.22, 95% CI 0.56-2.67), and in women with diabetes (OR 8.10, 95% CI 4.10-16.01, for BMI ≥30 versus <25 kg/m(2) ) than in those without diabetes (OR 2.95, 95% CI 2.44-3.56). The relation was best fitted by a cubic model, although after the exclusion of the 5% upper and lower tails, it was best fitted by a linear model. CONCLUSIONS: The results of this study confirm a role of elevated BMI in the aetiology of endometrial cancer and suggest that the risk in obese women increases in a cubic nonlinear fashion. The relation was stronger in never-users of oral contraceptives and in women with diabetes. TWEETABLE ABSTRACT: Risk of endometrial cancer increases with elevated body weight in a cubic nonlinear fashion.

Literature review on permanent magnet generators design and dynamic behavior

This paper is a literature review which describes the construction of state of the art of permanent magnet generators and motors constructing and discusses the current and possible application of these machines in industry. Permanent magnet machines are a well-know class of rotating and linear electric machines used for many years in industrial applications. A particular interest for permanent magnet generators is connected with wind mills, which seem to be becoming increasingly popular nowadays. Geared and direct-driven permanent magnet generators are described. A classification of direct-driven permanent magnet generators is given. Design aspects of permanent magnet generators are presented. Permanent magnet generators for wind turbines designs are highlighted. Dynamics and vibration problems of permanent magnet generators covered in literature are presented. The application of the Finite Element Method for mechanical problems solution in the field of permanent magnet generators is discussed.

A Headspace needle-trap method for the analysis of volatile organic compounds in whole blood

Needle trap devices (NTDs) are a relatively new and promising tool for headspace (HS) analysis. In this study, a dynamic HS sampling procedure is evaluated for the determination of volatile organic compounds (VOCs) in whole blood samples. A full factorial design was used to evaluate the influence of the number of cycles and incubation time and it is demonstrated that the controlling factor in the process is the number of cycles. A mathematical model can be used to determine the most appropriate number of cycles required to adsorb a prefixed amount of VOCs present in the HS phase whenever quantitative adsorption is reached in each cycle. Matrix effect is of great importance when complex biological samples, such as blood, are analyzed. The evaluation of the salting out effect showed a significant improvement in the volatilization of VOCs to the HS in this type of matrices. Moreover, a 1:4 (blood:water) dilution is required to obtain quantitative recoveries of the target analytes when external calibration is used. The method developed gives detection limits in the 0.020–0.080 μg L−1 range (0.1–0.4 μg L−1 range for undiluted blood samples) with appropriate repeatability values (RSD < 15% at high level and <23% at LOQ level). Figure of merits of the method can be improved by using a smaller phase ratio (i.e., an increase in the blood volume and a decrease in the HS volume), which lead to lower detection limits, better repeatability values and greater sensibility. Twenty-eight blood samples have been evaluated with the proposed method and the results agree with those indicated in other studies. Benzene was the only target compound that gave significant differences between blood levels detected in volunteer non-smokers and smokers

Fourier self-deconvolution in coherent anti-Stokes Raman scattering spectrum analysis

Coherent anti-Stokes Raman scattering (CARS) microscopy is rapidly developing into a unique microscopic tool in biophysics, biology and the material sciences. The nonlinear nature of CARS spectroscopy complicates the analysis of the received spectra. There were developed mathematical methods for signal processing and for calculations spectra. Fourier self-deconvolution is a special high pass FFT filter which synthetically narrows the effective trace bandwidth features. As Fourier self-deconvolution can effectively reduce the noise, which may be at a higher spatial frequency than the peaks, without losing peak resolution. The idea of the work is to experiment the possibility of using wavelet decomposition in spectroscopic for background and noise removal, and Fourier transformation for linenarrowing.

Full instability behavior of N-dimensional dynamical systems with a one-directional nonlinear vector field

We show how certain N-dimensional dynamical systems are able to exploit the full instability capabilities of their fixed points to do Hopf bifurcations and how such a behavior produces complex time evolutions based on the nonlinear combination of the oscillation modes that emerged from these bifurcations. For really different oscillation frequencies, the evolutions describe robust wave form structures, usually periodic, in which selfsimilarity with respect to both the time scale and system dimension is clearly appreciated. For closer frequencies, the evolution signals usually appear irregular but are still based on the repetition of complex wave form structures. The study is developed by considering vector fields with a scalar-valued nonlinear function of a single variable that is a linear combination of the N dynamical variables. In this case, the linear stability analysis can be used to design N-dimensional systems in which the fixed points of a saddle-node pair experience up to N21 Hopf bifurcations with preselected oscillation frequencies. The secondary processes occurring in the phase region where the variety of limit cycles appear may be rather complex and difficult to characterize, but they produce the nonlinear mixing of oscillation modes with relatively generic features

A panel data analysis of FDI and informal labor markets

The objective of this paper is to examine whether informal labor markets affect the flows of Foreign Direct Investment (FDI), and also whether this effect is similar in developed and developing countries. With this aim, different public data sources, such as the World Bank (WB), and the United Nations Conference on Trade and Development (UNCTAD) are used, and panel econometric models are estimated for a sample of 65 countries over a 14 year period (1996-2009). In addition, this paper uses a dynamic model as an extension of the analysis to establish whether such an effect exists and what its indicators and significance may be.

Comparison Structure Analysis

This study presents an automatic, computer-aided analytical method called Comparison Structure Analysis (CSA), which can be applied to different dimensions of music. The aim of CSA is first and foremost practical: to produce dynamic and understandable representations of musical properties by evaluating the prevalence of a chosen musical data structure through a musical piece. Such a comparison structure may refer to a mathematical vector, a set, a matrix or another type of data structure and even a combination of data structures. CSA depends on an abstract systematic segmentation that allows for a statistical or mathematical survey of the data. To choose a comparison structure is to tune the apparatus to be sensitive to an exclusive set of musical properties. CSA settles somewhere between traditional music analysis and computer aided music information retrieval (MIR). Theoretically defined musical entities, such as pitch-class sets, set-classes and particular rhythm patterns are detected in compositions using pattern extraction and pattern comparison algorithms that are typical within the field of MIR. In principle, the idea of comparison structure analysis can be applied to any time-series type data and, in the music analytical context, to polyphonic as well as homophonic music. Tonal trends, set-class similarities, invertible counterpoints, voice-leading similarities, short-term modulations, rhythmic similarities and multiparametric changes in musical texture were studied. Since CSA allows for a highly accurate classification of compositions, its methods may be applicable to symbolic music information retrieval as well. The strength of CSA relies especially on the possibility to make comparisons between the observations concerning different musical parameters and to combine it with statistical and perhaps other music analytical methods. The results of CSA are dependent on the competence of the similarity measure. New similarity measures for tonal stability, rhythmic and set-class similarity measurements were proposed. The most advanced results were attained by employing the automated function generation – comparable with the so-called genetic programming – to search for an optimal model for set-class similarity measurements. However, the results of CSA seem to agree strongly, independent of the type of similarity function employed in the analysis.

Thermal behaviour of succinic acid, sodium succinate and its compounds with some bivalent transitions metal ions in dynamic N2 and CO2 atmospheres

Thermal stability and thermal decomposition of succinic acid, sodium succinate and its compounds with Mn(II), Fe(II), Co(II), Ni(II), Cu(II) and Zn(II) were investigated employing simultaneous thermogravimetry and differential thermal analysis (TG-DTA) in nitrogen and carbon dioxide atmospheres and TG-FTIR in nitrogen atmosphere. On heating, in both atmospheres the succinic acid melt and evaporate, while for the sodium succinate the thermal decomposition occurs with the formation of sodium carbonate. For the transition metal succinates the final residue up to 1180 ºC in N2 atmosphere was a mixture of metal and metal oxide in no simple stoichiometric relation, except for Zn compound, where the residue was a small quantity of carbonaceous residue. For the CO2 atmosphere the final residue up to 980 ºC was: MnO, Fe3O4, CoO, ZnO and mixtures of Ni, NiO and Cu, Cu2O.

Analysis and Control of Excitation, Field Weakening and Stability in Direct Torque Controlled Electrically Excited Synchronous Motor Drives

Direct torque control (DTC) is a new control method for rotating field electrical machines. DTC controls directly the motor stator flux linkage with the stator voltage, and no stator current controllers are used. With the DTC method very good torque dynamics can be achieved. Until now, DTC has been applied to asynchronous motor drives. The purpose of this work is to analyse the applicability of DTC to electrically excited synchronous motor drives. Compared with asynchronous motor drives, electrically excited synchronous motor drives require an additional control for the rotor field current. The field current control is called excitation control in this study. The dependence of the static and dynamic performance of DTC synchronous motor drives on the excitation control has been analysed and a straightforward excitation control method has been developed and tested. In the field weakening range the stator flux linkage modulus must be reduced in order to keep the electro motive force of the synchronous motor smaller than the stator voltage and in order to maintain a sufficient voltage reserve. The dynamic performance of the DTC synchronous motor drive depends on the stator flux linkage modulus. Another important factor for the dynamic performance in the field weakening range is the excitation control. The field weakening analysis considers both dependencies. A modified excitation control method, which maximises the dynamic performance in the field weakening range, has been developed. In synchronous motor drives the load angle must be kept in a stabile working area in order to avoid loss of synchronism. The traditional vector control methods allow to adjust the load angle of the synchronous motor directly by the stator current control. In the DTC synchronous motor drive the load angle is not a directly controllable variable, but it is formed freely according to the motor’s electromagnetic state and load. The load angle can be limited indirectly by limiting the torque reference. This method is however parameter sensitive and requires a safety margin between the theoretical torque maximum and the actual torque limit. The DTC modulation principle allows however a direct load angle adjustment without any current control. In this work a direct load angle control method has been developed. The method keeps the drive stabile and allows the maximal utilisation of the drive without a safety margin in the torque limitation.

Analysis on firm innovation boundaries

Innovation has been widely recognized as an important driver of firm competitiveness, and the firm’s internal research and development (R&D) activities are often considered to have a critical role in innovation activities. Internal R&D is, however, not the source of innovation as firms may tap into knowledge necessary for innovation also through various types of sourcing agreements or by collaborating with other organizations. The objective of this study is to analyze the way firms go about organizing efficiently their innovation boundaries. Within this context, the analysis is focused, firstly, on the relation between innovation boundaries and firm innovation performance and, secondly, on the factors explaining innovation boundary organization. The innovation literature recognizes that the sources of innovation depend on the nature of technology but does not offer a sufficient tool for analyzing innovation boundary options and their efficiency. Thus, this study suggests incorporating insights from transaction cost economics (TCE) complemented with dynamic governance costs and benefits into the analysis. The thesis consists of two parts. The first part introduces the background of the study, research objectives, an overview of the empirical studies, and the general conclusions of the study. The second part is formed of five publications. The overall results firstly indicate that although the relation between firm innovation boundary options is partly industry sector-specific, the firm level search strategies and knowledge transfer capabilities are important for innovation performance independently of the sector. Secondly, the results show that the attributes suggested by TCE alone do not offer a sufficient explanation of innovation boundary selection, especially under conditions of high levels of (radical) uncertainty. Based on the results, the dynamic governance cost and benefit framework complements the static TCE when firm innovation boundaries are scrutinized.

MCMC Analysis for Optimization of Stochastic Models

In any decision making under uncertainties, the goal is mostly to minimize the expected cost. The minimization of cost under uncertainties is usually done by optimization. For simple models, the optimization can easily be done using deterministic methods.However, many models practically contain some complex and varying parameters that can not easily be taken into account using usual deterministic methods of optimization. Thus, it is very important to look for other methods that can be used to get insight into such models. MCMC method is one of the practical methods that can be used for optimization of stochastic models under uncertainty. This method is based on simulation that provides a general methodology which can be applied in nonlinear and non-Gaussian state models. MCMC method is very important for practical applications because it is a uni ed estimation procedure which simultaneously estimates both parameters and state variables. MCMC computes the distribution of the state variables and parameters of the given data measurements. MCMC method is faster in terms of computing time when compared to other optimization methods. This thesis discusses the use of Markov chain Monte Carlo (MCMC) methods for optimization of Stochastic models under uncertainties .The thesis begins with a short discussion about Bayesian Inference, MCMC and Stochastic optimization methods. Then an example is given of how MCMC can be applied for maximizing production at a minimum cost in a chemical reaction process. It is observed that this method performs better in optimizing the given cost function with a very high certainty.

Analysis of the relationship intensity, duration, frequency of disaggregated daily rainfall in southern Rio Grande do Sul, Brazil

The intensity, duration, and frequency relationship (IDF) of rainfall occurrence may be done through continuous records of pluviographs or daily pluviometer values . The objective of this study was to estimate the intensity-duration-frequency relationships of precipitation, using the method of daily rainfall disaggregation, at weather stations located to the southern half of the state of Rio Grande do Sul; comparing them with those obtained by rain gauge records, in places considered homogeneous from the meteorological point of view. The IDF equation parameters were estimated from daily rainfall disaggregation data, using the method of nonlinear optimization. To validate the equations confidence indices and efficiency and the "t" Student test, among maximum intensity values obtained from the disaggregated daily rainfall durations of 10; 30; 60 min and 6; 12 and 24 h and those extracted from existing IDF equations. For all studied stations and return periods, the trust index values were regarded as "optimal", i.e., greater than 0.85. The maximal intensity of rainfall obtained by daily rainfall disaggregation have similarity with those obtained by relations IDF standards. Thus, the method constitutes a feasible alternative in obtaining the IDF relationships.

Dynamic Hedging with Credit Default Swaps: Empirical Evidence from the European Monetary Union

This thesis investigates the effectiveness of time-varying hedging during the financial crisis of 2007 and the European Debt Crisis of 2010. In addition, the seven test economies are part of the European Monetary Union and these countries are in different economical states. Time-varying hedge ratio was constructed using conditional variances and correlations, which were created by using multivariate GARCH models. Here we have used three different underlying portfolios: national equity markets, government bond markets and the combination of these two. These underlying portfolios were hedged by using credit default swaps. Empirical part includes the in-sample and out-of-sample analysis, which are constructed by using constant and dynamic models. Moreover, almost in every case dynamic models outperform the constant ones in the determination of the hedge ratio. We could not find any statistically significant evidence to support the use of asymmetric dynamic conditional correlation model. In addition, our findings are in line with prior literature and support the use of time-varying hedge ratio. Finally, we found that in some cases credit default swaps are not suitable instruments for hedging and they act more as a speculative instrument.

Dynamic Ownership in Family Business System – A Portfolio Business Approach

Family businesses are among the longest-lived most prevalent institutions in the world and they are an important source of economic development and growth. Ownership is a key to the business life of the firm and also one main key in family business definition. There is only a little portfolio entrepreneurship or portfolio business research within family business context. The absence of empirical evidence on the long-term relationship between family ownership and portfolio development presents an important gap in the family business literature. This study deals with the family business ownership changes and the development of portfolios in the family business and it is positioned in to the conversation of family business, growth, ownership, management and strategy. This study contributes and expands the existing body of theory on family business and ownership. From the theoretical point of view this study combines insights from the fields of portfolio entrepreneurship, ownership, and family business and integrate them. This crossfertilization produces interesting empirical and theoretical findings that can constitute a basis for solid contributions to the understanding of ownership dynamics and portfolio entrepreneurship in family firms. The research strategy chosen for this study represents longitudinal, qualitative, hermeneutic, and deductive approaches.The empirical part of study is using a case study approach with embedded design, that is, multiple levels of analysis within a single study. The study consists of two cases and it begins with a pilot case which will form a preunderstanding on the phenomenon. Pilot case develops the methodology approach to build in the main case and the main case will deepen the understanding of the phenomenon. This study develops and tests a research method of family business portfolio development focusing on investigating how ownership changes are influencing to the family business structures over time. This study reveals the linkages between dimensions of ownership and how they give rise to portfolio business development within the context of the family business. The empirical results of the study suggest that family business ownership is dynamic and owners are using ownership as a tool for creating business portfolios.