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A follow-up study of erosive prepyloric changes (EPC) was undertaken in 60 patients who originally presented with non-ulcer dyspepsia and EPC grade 2 or 3. After 45 (range, 24–60) months EPC grade 2 or 3 was still present in 80% of the patients, and 82% had unchanged dyspeptic symptoms. Most of the patients (77%) experienced a ‘stressful’ life situation. None of the patients had developed peptic ulceration during the follow-up period, and there were no indications of active or healed ulcerations or cancer at the follow-up study. The results indicate that EPC are chronic changes in the gastric mucosa closely associated with long-lasting dyspeptic symptoms.

Current approaches for the treatment of chronic lymphocytic leukemia (CLL) have greatly improved the prognosis for survival, but some patients remain refractive to these therapeutic regimens. Hence, in addition to reducing the long-term sideeffects of therapeutics for all leukemia patients, there is an urgent need for novel therapeutic strategies for difficult-to-treat leukemia cases. Due to the cytotoxicity of drugs, the major challenge currently is to deliver the therapeutic agents to neoplastic cells while preserving the viability of non-malignant cells. In this study, we propose a therapeutic approach in which high doses of hydroxychloroquine and chlorambucil were loaded into biodegradable polymeric nanoparticles coated with an anti-CD20 antibody.We first demonstrated the ability of the nanoparticles to target and internalize in tumor B-cells. Moreover, these nanoparticles could kill not only p53-mutated/deleted leukemia cells expressing a low amount of CD20, but also circulating primary cells isolated from chronic lymphocytic leukemia patients. The safety of these nanoparticles was also demonstrated in healthy mice, and their therapeutic effects were shown in a new model of aggressive leukemia. These results showed that anti-CD20 nanoparticles containing hydroxychloroquine and chlorambucil can be effective in controlling aggressive leukemia and provided a rationale for adopting this approach for the treatment of other B-cell disorders. [Figure not available: see fulltext.]

The aim of this study was to determine if Rituximab coated Biodegradable Nanoparticles (BNPs) loaded with Chlorambucil and Hydroxychloroquine could induce apoptosis of B-Chronic Lymphocytic Leukemia (B-CLL), MEC-1 and BJAB cells in vitro and evaluate their toxic and therapeutic effects on a Human/Mouse Model of Burkitt Lymphoma at an exploratory, proof of concept scale. We found that Rituximab-Chlorambucil-Hydroxychloroquine BNPs induce a decrease in cell viability of malignant B cells in a dose-dependent manner. The mediated cytotoxicity resulted from apoptosis, and was confirmed by monitoring the B-CLL cells after Annexin V/propidium iodide staining. Additional data revealed that these BNPs were non toxic for healthy animals, and had prolonged survival in this mice model of human lymphoma.

In the current study, we use a large-eddy simulation of a wind-farm boundary layer to generate the fluctuating wind fields that are observed at different turbines in the wind farm. Using these wind fields as inputs, we focus on the development of a benchmark framework in which we explore the trade-off between high energy extraction and low variability using optimal control of multiple turbines subject to a turbulent wind field. The controls variables that are optimized are the electric torque and the pitch angles of the individual turbines over time horizons of 10 minutes. Moreover, both optimal control of individual turbines and coordinated optimal control of groups of turbines are investigated. Optimal control results are presented in terms of Pareto fronts that show optimal trade-offs between energy extraction and power smoothing. We find that power variations can be significantly reduced with limited loss of extracted energy. Moreover, coordinated control can effectively reduce fluctuations over longer time scales. For instance, considering 24 optimally coordinated turbines, variability at a time scale of 50 seconds is reduced 4 times more than the normal statistical reduction of 24 uncoordinated turbines. © Published under licence by IOP Publishing Ltd. ispartof: Journal of Physics: Conference Series vol:524 issue:1 ispartof: The Science of Making Torque from Wind location:Lyngby date:18 Jun - 20 Jun 2014 status: published

In this study we consider large wind farms in a conventionally neutral atmospheric boundary layer. In large wind farms the energy extracted by the turbines is dominated by downward vertical turbulent transport of kinetic energy from the airflow above the farm. However, atmospheric boundary layers are almost always capped by an inversion layer which slows down the entrainment rate and counteracts boundary layer growth. In a suite of large eddy simulations the effect of the strength of the capping inversion on the boundary layer and on the performance of a large wind farm is investigated. For simulations with and without wind turbines the results indicate that the boundary layer growth is effectively limited by the capping inversion and that the entrainment rate depends strongly on the inversion strength. The power output of wind farms is shown to decrease for increasing inversions. © Published under licence by IOP Publishing Ltd. ispartof: Journal of Physics: Conference Series vol:524 issue:1 ispartof: The Science of Making Torque from Wind location:Lyngby date:18 Jun - 20 Jun 2014 status: published

In this paper a new paradigm for prediction of wind turbine wakes is proposed, which is based on a reduced order model (ROM) embedded in a Kalman filter. The ROM is evaluated by means of dynamic mode decomposition performed on high fidelity LES numerical simulations of wind turbines operating under different operational regimes. The ROM enables to capture the main physical processes underpinning the downstream evolution and dynamics of wind turbine wakes. The ROM is then embedded within a Kalman filter in order to produce a time-marching algorithm for prediction of wind turbine wake flows. This data-driven algorithm enables data assimilation of new measurements simultaneously to the wake prediction, which leads to an improved accuracy and a dynamic update of the ROM in presence of emerging coherent wake dynamics observed from new available data. Thanks to its low computational cost, this numerical tool is particularly suitable for real-time applications, control and optimization of large wind farms.

Stereo particle image velocimetry is used in a wind-tunnel to study boundary layer effects in the wake behind a vertical axis wind turbine. The turbine is a three-bladed giromill with a solidity of 1.18. The wake is studied for a tip speed ratio of 2 and an average chord Reynolds number of 1.6 × 104. The velocity deficit and turbulence levels in the horizontal plane are observed to be strongly asymmetrical with two strong peaks corresponding to the two halves of the rotor where blades move either towards the oncoming flow or away from it. The stronger peak is measured behind the blades moving upstream, however this region also benefits from a greater rate of re-energization. Due to the incoming boundary layer profile, momentum is also entrained downwards into the wake from above and aids with the recovery of the core of the wake.

In this work a technique capable to investigate the near-wake stability properties of a wind turbine immersed in the atmospheric boundary layer is presented. Specifically, a 2D local spatial stability analysis is developed in order to take into account typical flow features of real operating wind turbines, such as the presence of the atmospheric boundary layer and the turbulence heterogeneity of the oncoming wind. This stability analysis can be generally applied on either experimental measurements or numerical data. In this paper it was carried out on wind tunnel experiments, for which a downscaled wind turbine is immersed in a turbulent boundary layer. Through spatial stability analysis, the dominant mode in the near wake, i.e. the most amplified one, is characterized and its frequency matches the hub-vortex instability frequency measured in the wind tunnel. As in the case of [10], where an axisymmetric wake condition was investigated, the hub-vortex instability results in a single-helical mode.

In this study, a new analytical approach is presented and validated to predict wind farm power production. The new model is an extension of the recently proposed by Bastankhah and Porte-Agel for a single wake. It assumes a self-similar Gaussian shape of the velocity deficit and satisfies conservation of mass and momentum. To estimate the velocity deficit in the wake, this model needs the local wake growth rate parameter which is calculated based on the local turbulence intensity in the wind farm. The interaction of the wakes is modeled by use of the velocity deficit superposition principle Finally, the power curve is used to estimate the power production from the wind turbines. The wind farm model is compared to large-eddy simulation (LES) data and measurments of Horns Rev wind farm for a wide range of wind directions. Reasonable agreement between the proposed analytical model, LES data and measurments is obtained. This prediction is also found to be substantially better than the one obtained with a commonly used wind farm wake model.

The effect of selecting the twist angle and chord length distributions on the wind turbine blade design was investigated by performing aerodynamic optimization of a two-bladed stall regulated horizontal axis wind turbine. Twist angle and chord length distributions were defined using Bezier curve using 3, 5, 7 and 9 control points uniformly distributed along the span. Optimizations performed using a micro-genetic algorithm with populations composed of 5, 10, 15, 20 individuals showed that, the number of control points clearly affected the outcome of the process; however the effects were different for different population sizes. The results also showed the superiority of micro-genetic algorithm over a standard genetic algorithm, for the selected population sizes. Optimizations were also performed using a macroevolutionary algorithm and the resulting best blade design was compared with that yielded by micro-genetic algorithm.

In the current study, an Actuator-Line Model (ALM) is implemented in our in-house pseudo-spectral LES solver SP-WIND, including a turbine controller. Below rated wind speed, turbines are controlled by a standard-torque-controller aiming at maximum power extraction from the wind. Above rated wind speed, the extracted power is limited by a blade pitch controller which is based on a proportional-integral type control algorithm. This model is used to perform a series of single turbine and wind farm simulations using the NREL 5MW turbine. First of all, we focus on below-rated wind speed, and investigate the effect of the farm layout on the controller calibration curves. These calibration curves are expressed in terms of nondimensional torque and rotational speed, using the mean turbine-disk velocity as reference. We show that this normalization leads to calibration curves that are independent of wind speed, but the calibration curves do depend on the farm layout, in particular for tightly spaced farms. Compared to turbines in a lone-standing set-up, turbines in a farm experience a different wind distribution over the rotor due to the farm boundary-layer interaction. We demonstrate this for fully developed wind-farm boundary layers with aligned turbine arrangements at different spacings (5D, 7D, 9D). Further we also compare calibration curves obtained from full farm simulations with calibration curves that can be obtained at a much lower cost using a minimal flow unit. © Published under licence by IOP Publishing Ltd. ispartof: Journal of Physics: Conference Series vol:524 issue:1 ispartof: The Science of Making Torque from Wind location:Lyngby date:18 Jun - 20 Jun 2014 status: published

Aerodynamic optimization of wind farm layout is a crucial task to reduce wake effects on downstream wind turbines, thus to maximize wind power harvesting. However, downstream evolution and recovery of wind turbine wakes are strongly affected by the characteristics of the incoming atmospheric boundary layer (ABL) flow, such as wind shear and turbulence intensity, which are in turn affected by the ABL thermal stability. In order to characterize the downstream evolution of wakes produced by full-scale wind turbines under different atmospheric conditions, wind velocity measurements were performed with three wind LiDARs. The volumetric scans are performed by continuously sweeping azimuthal and elevation angles of the LiDARs in order to cover a 3D volume that includes the wind turbine wake. The minimum wake velocity deficit is then evaluated as a function of the downstream location for different atmospheric conditions. It is observed that the ABL thermal stability has a significant effect on the wake evolution, and the wake recovers faster under convective conditions.

Current B-cell disorder treatments take advantage of dose-intensive chemotherapy regimens and immunotherapy via use of monoclonal antibodies. Unfortunately, they may lead to insufficient tumor distribution of therapeutic agents, and often cause adverse effects on patients. In this contribution, we propose a novel therapeutic approach in which relatively high doses of Hydroxychloroquine and Chlorambucil were loaded into biodegradable nanoparticles coated with an anti-CD20 antibody. We demonstrate their ability to effectively target and internalize in tumor B-cells. Moreover, these nanoparticles were able to kill not only p53 mutated/deleted lymphoma cell lines expressing a low amount of CD20, but also circulating primary cells purified from chronic lymphocitic leukemia patients. Their safety was demonstrated in healthy mice, and their therapeutic effects in a new model of Burkitt's lymphoma. The latter serves as a prototype of an aggressive lympho-proliferative disease. In vitro and in vivo data showed the ability of anti-CD20 nanoparticles loaded with Hydroxychloroquine and Chlorambucil to increase tumor cell killing in comparison to free cytotoxic agents or Rituximab. These results shed light on the potential of anti-CD20 nanoparticles carrying Hydroxychloroquine and Chlorambucil for controlling a disseminated model of aggressive lymphoma, and lend credence to the idea of adopting this therapeutic approach for the treatment of B-cell disorders.

In the wind energy industry there is a growing need for real-time predictions of wind turbine wake flows in order to optimize power plant control and inhibit detrimental wake interactions. To this aim, a data-driven RANS approach is proposed in order to achieve very low computational costs and adequate accuracy through the data assimilation procedure. The RANS simulations are implemented with a classical Boussinesq hypothesis and a mixing length turbulence closure model, which is calibrated through the available data. High-fidelity LES simulations of a utility-scale wind turbine operating with different tip speed ratios are used as database. It is shown that the mixing length model for the RANS simulations can be calibrated accurately through the Reynolds stress of the axial and radial velocity components, and the gradient of the axial velocity in the radial direction. It is found that the mixing length is roughly invariant in the very near wake, then it increases linearly with the downstream distance in the diffusive region. The variation rate of the mixing length in the downstream direction is proposed as a criterion to detect the transition between near wake and transition region of a wind turbine wake. Finally, RANS simulations were performed with the calibrated mixing length model, and a good agreement with the LES simulations is observed.

Dynamics and instabilities occurring in the near-wake of wind turbines have a crucial role for the wake downstream evolution, and for the onset of far-wake instabilities. Furthermore, wake dynamics significantly affect the intra-wind farm wake flow, wake interactions and potential power losses. Therefore, the physical understanding and predictability of wind turbine wake instabilities become a nodal point for prediction of wind power harvesting and optimization of wind farm layout. This study is focused on the prediction of the hub vortex instability encountered within wind turbine wakes under different operational conditions of the wind turbine. Linear stability analysis of the wake flow is performed by means of a novel approach that enables to take effects of turbulence on wake instabilities into account. Stability analysis is performed by using as base flow the time-averaged wake velocity field at a specific downstream location. The latter is modeled through Carton-McWilliams velocity profiles by mimicking the presence of the hub vortex and helicoidal tip vortices, and matching the wind turbine thrust coefficient predicted through the actuator disc model. The results show that hub vortex instability is promoted by increasing the turbine thrust coefficient. Indeed, a larger aerodynamic load produces an enhanced wake velocity deficit and axial shear, which are considered the main sources for the wake instability. Nonetheless, wake swirl also promotes hub vortex instability, and it can also affect the azimuthal wavenumber of the most unstable mode.