2-Phenyloxazole-4-carboxamide as a Scaffold for Selective Inhibition of Human Monoamine Oxidase B

A series of 2-phenyloxazoles bearing an amide group at position 4 were designed and synthesized for evaluation as potential inhibitors of human recombinant monoamine oxidases (hrMAOs). Results of kinetics experiments demonstrated that all compounds behave as competitive MAO inhibitors, with good selectivity toward the MAO-B isoform. The most potent and selective derivatives are characterized by inhibition constant (K_i) values in the sub-micromolar range and a good selectivity index (K_{i MAO-A}/K_{i MAO-B}>50). Some derivatives were also found to be able to inhibit MAO activity in nerve growth factor (NGF)-differentiated PC12 cells, taken as a model of neuronal cells. In particular, 2-(2-hydroxyphenyl)-N-phenyloxazole-4-carboxamide (compound 4 a ) may be a promising new scaffold, exerting the highest selectivity and inhibitory effect toward MAOs in NGF-differentiated PC12 cell lysates, without compromising cell viability. Molecular docking analysis allowed a rationalization of the experimentally observed binding affinity and selectivity.     

Fuzzy Learning of Talbot Effect Guides Optimal Mask Design for Proximity Field Nanopatterning Lithography

Processing methods used in photonics and nanotechnology possess many limitations restricting their application areas such as high cost, inability to produce fine details, problems with scalability, and long processing time. Proximity field nanopatterning is a lithography method which surpasses these limitations. By using interference patterns produced by a two-dimensional phase mask, the technique is able to generate a submicron detailed exposure on a millimeter-size slab of light sensitive photopolymer, which is then developed like a photographic plate to reveal three-dimensional interference patterns from the phase mask. While it is possible to use simulations to obtain the interference patterns produced by a phase mask, realizing the mask dimensions necessary for producing a desired interference pattern is analytically challenging due to the intricacies of light interactions involved in producing the final interference pattern. An alternative method is to iteratively optimize the phase mask until the interference patterns obtained converge to the desired pattern. However, depending on the optimization technique used, one either risks a significant probability of failure or requires a prohibitive number of iterations. We argue that an optimization technique that is to take advantage of the physics of the problem using machine learning methods (here fuzzy learning) can lead to competent mask design. This technique is described in this letter.     

Neural adaptive regulation of unknown nonlinear dynamical systems

With this paper we extend our previous work on the subject, by including the case where the number of control inputs is different from the number of states which is frequently faced in control engineering problems. Uniform ultimate boundedness of the state and uniform boundedness of all other signals in the closed loop is guaranteed. Robustness of our algorithm due to the presence of a modeling error term which has linear growth with unknown growth coefficient is also established. Finally, the applicability of our control scheme is highlighted via simulation results.     

A new efficient dcvs circuit synthesis technique used for an improved implementation of a serial/parallel multiplier

This paper describes an efficient technique for the design of fault-secure VLSI circuits based on differential cascode voltage switch (DCVS) logic. We propose a new synthesis method for constructing DCVS circuits with a near-optimal transistor count based on binary decision diagrams (BDDs). the time and memory resources required are very low, making the technique practical even for PC-based synthesis tools. This method is the basis for a CAD tool that allows automatic synthesis of fault-secure circuits based on the DCVS technology. We finally present an improved design and implementation of a 2's complement serial/parallel multiplier as an application of the proposed technique and algorithm.     

Phase I trial of weekly scheduling and pharmacokinetics of titanocene dichloride in patients with advanced cancer

PURPOSE: To determine the maximum-tolerated dose (MTD) and the dose-limiting toxicities (DLTs) of a weekly schedule of titanocene dichloride (TD) and to define the pharmacokinetics of titanium in plasma and urine. PATIENTS AND METHODS: Twenty patients with a median age of 58 years received 83 courses of TD. TD was given as 1-hour infusion at escalating doses from 70 to 185 mg/m2/wk. Pharmacokinetic analysis was performed in eight patients for total plasma titanium (TPTi) and in three patients for ultrafiltrable titanium (UFTi). RESULTS: At the fifth dose level (185 mg/m2/wk), a variety of DLTs were seen in five patients: fatigue in three, bilirubinemia in one, and hypokalemia in two. A further six patients were treated at 140 mg/m2; only one had dose-limiting creatinine elevation and this dose was therefore defined as the MTD. No myelosuppression or alopecia were observed. One patient with adenocarcinoma of unknown primary had a minor response. Pharmacokinetic analysis showed that TPTi maximum concentration (Cmax) values were linear with dose and elimination of TPTi was triphasic with a long terminal half-life (t1/2; median, 165 hours; range, 89 to 592). Between 7% and 24.3% of the total of administered titanium was eliminated in urine over the first 24 hours. In contrast, UFTi elimination was described by a one-compartment model with a t1/2 of 0.41 hours; peak levels of UFTi were 5.2% +/- 2.5% those of TPTi. CONCLUSION: The MTD of TD given on a weekly schedule is 140 mg/m2, with cumulative, but reversible creatinine and bilirubin elevation being the DLTs.     

Non-manual cues in automatic sign language recognition

Present work deals with the incorporation of non-manual cues in automatic sign language recognition. More specifically, eye gaze, head pose, and facial expressions are discussed in relation to their grammatical and syntactic function and means of including them in the recognition phase are investigated. Computer vision issues related to extracting facial features, eye gaze, and head pose cues are presented and classification approaches for incorporating these non-manual cues into the overall Sign Language recognition architecture are introduced.     

Efficiently adapting graphical models for selectivity estimation

Query optimizers rely on statistical models that succinctly describe the underlying data. Models are used to derive cardinality estimates for intermediate relations, which in turn guide the optimizer to choose the best query execution plan. The quality of the resulting plan is highly dependent on the accuracy of the statistical model that represents the data. It is well known that small errors in the model estimates propagate exponentially through joins, and may result in the choice of a highly sub-optimal query execution plan. Most commercial query optimizers make the attribute value independence assumption: all attributes are assumed to be statistically independent. This reduces the statistical model of the data to a collection of one-dimensional synopses (typically in the form of histograms), and it permits the optimizer to estimate the selectivity of a predicate conjunction as the product of the selectivities of the constituent predicates. However, this independence assumption is more often than not wrong, and is considered to be the most common cause of sub-optimal query execution plans chosen by modern query optimizers. We take a step towards a principled and practical approach to performing cardinality estimation without making the independence assumption. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution over all the attributes in the database into small, usually two-dimensional distributions, without a significant loss in estimation accuracy. We show how to efficiently construct such a graphical model from the database using only two-way join queries, and we show how to perform selectivity estimation in a highly efficient manner. We integrate our algorithms into the PostgreSQL DBMS. Experimental results indicate that estimation errors can be greatly reduced, leading to orders of magnitude more efficient query execution plans in many cases. Optimization time is kept in the range of tens of milliseconds, making this a practical approach for industrial-strength query optimizers.     

Switching networked attitude control of an unmanned quadrotor

In this article, a switching networked attitude controller for an unmanned quadrotor over a wireless sensor network is presented. To deal with the network induced time varying delays, the quadrotor is being modeled as a switching time varying linear system, while the applied switching output feedback control scheme, is calculated based on Linear Matrix Inequalities, and is able to guarantee the stability of the quadrotor under arbitrary changes in the time delays.     

<Emphasis Type="Italic">HumanPT</Emphasis>: An Open-source,HumanPT Architecture-based,Robotic Application for Low Cost Robotic Tasks

In this paper, first HumanPT architecture for low cost robotic applications is presented. HumanPT architecture differs than other architectures because it is implemented on existing robotic systems (robot  robotic controller) and exploits the minimum communication facilities for real-time control that these systems provide. It is based on well-known communication methods like serial communication (USB, RS232, IEEE-1394) and windows sockets (server–client model) and permits an important number of different type of components like actuators, sensors and particularly vision systems to be connected in a robotic system. The operating system (OS) used is Microsoft Windows, the most widely spread OS. The proposed architecture exploits features of this OS that is not a real-time one, to ensure – in case that the robotic system provide such a facility – control and real time communication with the robotic system controller and to integrate by means of sensors and actuators an important number of robotic tasks and procedures. As implementation of this architecture, HumanPT robotic application and experimental results concerning its performance and its implementation in real tasks are provided. HumanPT robotic application, developed in Visual C++, is an integrated, but simultaneously an open-source software that can be adapted in different types of robotic systems. An important number of robotic tasks or procedures including sensors and particularly vision systems can be generated and executed. Small enterprises by means of the proposed architecture and the open source software can be automated at low cost enhancing in this way their production.