Crystal structure of the complex of the cyclin D-dependent kinase Cdk6 bound to the cell-cycle inhibitor p19INK4d

The crystal structure of the cyclin D-dependent kinase Cdk6 bound to the p19 INK4d protein has been determined at 1.9 A resolution. The results provide the first structural information for a cyclin D-dependent protein kinase and show how the INK4 family of CDK inhibitors bind. The structure indicates that the conformational changes induced by p19INK4d inhibit both productive binding of ATP and the cyclin-induced rearrangement of the kinase from an inactive to an active conformation. The structure also shows how binding of an INK4 inhibitor would prevent binding of p27Kip1, resulting in its redistribution to other CDKs. Identification of the critical residues involved in the interaction explains how mutations in Cdk4 and p16INK4a result in loss of kinase inhibition and cancer.     

Posterior surgery for the treatment of thoracolumbar pathologic fractures in metastatic patients

A total of 25 pathologic fractures in patients affected with thoracolumbar vertebral metastases associated with neurologic deficit are reported. None of the pathologic fractures were stable, while 14 were unstable and 11 were very unstable. Decompression with intralesional excision of the neoplastic mass compressing the dural sac was performed in all of the cases. Posterior stabilization was performed in the first cases using systems of sublaminar segmental fixation, and thereafter using systems of pedicle fixation. Removal of the vertebral body followed by anterior fusion after preventive posterior stabilization was performed in 2 cases. Pain symptoms regressed in 85% of the cases and in more than 50% of the patients there was improvement in the neurologic findings and in vertebral deformity consequent to fracture. Mean survival rate was 12 months. Despite the limited number of cases posterior stabilization of pathological fractures is a good choice of treatment in patients with severe neurologic deficit.     

A hybrid receding-horizon control scheme for nonlinear systems

A hybrid receding-horizon control scheme for nonlinear discrete-time systems is proposed. Whereas a set of optimal feedback control functions is defined at the continuous level, a discrete-event controller chooses the best control action, depending on the current conditions of a plant and on possible external events. Such a two-level scheme is embedded in the structure of abstract hybrid systems, thus making it possible to prove a new asymptotic stability result for the hybrid receding-horizon control approach.     

Adaptive task-space metal strip-flatness control in cold multi-roll mill stands

The automatic flatness control system applied to cold multi-roll mills for the production of metal strips has been subject of research efforts in many directions since the last 30 years. An innovative multivariable control approach for this application context is presented by means of which potential stability problems of controllers based on least mean squares are addressed. The proposed methodology decomposes the array of strip-elongation measurements produced by a conventional shape-meter into orthogonal components corresponding to the main actuator directions in order to reduce the dimensionality of the problem. Then, the control actions are computed by solving a prioritized constrained quadratic optimization problem. In this regard, the control problem is reformulated within a task-space control formalism – originally conceived in the robotics context – for which very efficient solution procedures do exist. Furthermore, in order to account for the model uncertainties that typically affects this kind of systems, the model of the process is adapted on-line by a numerically robust technique. The adaptive task-space flatness control scheme dealt with in the paper has been already commissioned in several installations having different actuator configurations and the experimental results shows its effectiveness and its easy configurability.     

Neural approximations for infinite-horizon optimal control of nonlinear stochastic systems.

A feedback control law is proposed that drives the controlled vector v(t) of a discrete-time dynamic system to track a reference v(t)* over an infinite time horizon, while minimizing a given cost function. The behavior of v(t)* over time is completely unpredictable, Random noises act on the dynamic system and the state observation channel, which may be nonlinear. It is assumed that all such random vectors are mutually independent, and that their probability density functions are known. So general a non-LQG optimal control problem is very difficult to solve. The proposed solution is based on three main approximating assumptions: 1) the problem is stated in a receding-horizon framework where v(t)* is assumed to remain constant within a shifting-time window; 2) the control law is assigned a given structure (that of a multilayer feedforward neural net) in which a finite number of parameters have to be determined so as to minimize the cost function; and 3) the control law is given a limited memory, which prevents the amount of data to be stored from increasing over time. Errors resulting from the second and third assumptions are discussed, Due to the very general assumptions under which the control law is derived, we are not able to report stability results. However, simulation results show that the proposed method may constitute an effective tool for solving, to a sufficient degree of accuracy, a wide class of control problems traditionally regarded as difficult ones. An example of freeway traffic optimal control is given.     

Development of a new self-tuning control algorithm for finite and infinite horizon quadratic adaptive optimization

The theory of implicit models, introduced in previous papers, is used here in order to define a new adaptive control algorithm based on either m-step-ahead or infinite horizon LQ optimization and on recursive least squares identification techniques in the presence of systems having an ARMAX structure. The adaptive algorithm is based on the identification of a single ARX implicit model, which is defined as a model capable of representing the system input-output behaviour correctly only in certain closed-loop conditions. It is shown that, by properly structuring the algorithm, a single whitening (i.e. yielding a white residual sequence) possible convergence point exists coinciding with the optimal control law. Simple conditions assuring that a generic convergence point coincides with the above one are also provided, as well as preliminary simulation experience.     

Identification of neural dynamic models for fault detection and isolation: the case of a real sugar evaporation process

The paper deals with problems of fault detection of industrial processes using dynamic neural networks. The considered neural network has a feed-forward multi-layer structure and dynamic characteristics are obtained by using dynamic neuron models. Two optimisation problems are associated with neural networks. The first one is selection of a proper network structure which is solved by using information criteria such as the Akaike Information Criterion or the Final Prediction Error. In turn, the training of the network is performed by a stochastic approximation algorithm. The effectiveness of the proposed fault detection and isolation system is checked using real data recorded in Lublin Sugar Factory, Poland. Additionally, a comparison with alternative approaches is presented.     

Wet Adsorption of a Luminescent EuIII complex on Carbon Nanotubes Sidewalls

A Eu^III complex, tris‐dibenzoylmethane mono‐1,10‐phenanthroline‐europium(III) [Eu(DBM) ₃ (Phen)] , can be easily adsorbed in situ via hydrophobic interactions to single‐walled carbon nanotube (SWNT) surfaces from a methanol solution. The Eu^III‐containing material has been comprehensively characterized via thermogravimetric analysis (TGA), UV‐vis‐NIR absorption and luminescence spectroscopy, Raman spectroscopy, atomic force microscopy (AFM), high‐resolution transmission electron microscopy (HR‐TEM)), Z‐contrast scanning transmission electron microscopy (STEM) imaging, and energy dispersive X‐ray spectroscopy (EDS). The photophysical investigations revealed that the presence of a SWNT framework does not affect the lanthanide‐centered luminescence stemming from the characteristic electronic transitions within the 4f shell of the Eu^III ions. Such straightforward synthetic route leads to the preparation of luminescent SWNTs without significantly affecting the electronic and structural properties of the carbon framework, opening new possibilities of designing new classes of CNTs for biomedical applications.     

A parallel prefiltering approach for the identification of a biased sinusoidal signal: Theory and experiments

The problem of estimating the amplitude, frequency, and phase of an unknown sinusoidal signal from a noisy‐biased measurement is addressed in this paper by a family of parallel prefiltering schemes. The proposed methodology consists in using a pair of linear filters of specified order to generate a suitable number of auxiliary signals that are used to estimate—in an adaptive way—the frequency, the amplitude, and the phase of the sinusoid. Increasing the order of the prefilters improves the noise immunity of the estimator, at the cost of an increase of the computational complexity. Among the whole family of estimators realizable by varying the order of the filters, the simple parallel prefilters of orders 2 + 2 and 3 + 3 are discussed in detail, being the most attractive from the implementability point of view. The behavior of the two algorithms with respect to bounded external disturbances is characterized by input‐to‐state stability arguments. Finally, the effectiveness of the proposed technique is shown both by comparative numerical simulations and by a real experiment addressing the estimation of the frequency of the electrical mains from a noisy voltage measurement. Copyright © 2015 John Wiley & Sons, Ltd.