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.     

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.     

Generating a Visual Language of Performance-Driven Configurations for the Principal Façade of a Prototype Sustainable House

A performance-driven application of shape grammars is presented. A parametric shape grammar that generates a language of pattern designs for the principal façade of a prototype house—featuring a 5 × 20 matrix of electrochromic windows—based equally on performance and aesthetic criteria is described. The adjustment of the chromatism and light transmittance of each individual windowpane on the façade enables the adjustment of solar radiation at the house interior. The novel aspect of the grammar is that it encodes performance constraints of interior daylight illuminance and associates them to visual, symmetry principles of two-dimensional pattern generation. Twelve parametric rules account for the generation of the façade pattern language and five subclasses account for the symmetry of the patterns in the language.     

A New Neuro-FDS Definition for Indirect Adaptive Control of Unknown Nonlinear Systems Using a Method of Parameter Hopping

The indirect adaptive regulation of unknown nonlinear dynamical systems is considered in this paper. The method is based on a new neuro-fuzzy dynamical system (neuro-FDS) definition, which uses the concept of adaptive fuzzy systems (AFSs) operating in conjunction with high-order neural network functions (FHONNFs). Since the plant is considered unknown, we first propose its approximation by a special form of an FDS and then the fuzzy rules are approximated by appropriate HONNFs. Thus, the identification scheme leads up to a recurrent high-order neural network (RHONN), which however takes into account the fuzzy output partitions of the initial FDS. The proposed scheme does not require a priori experts' information on the number and type of input variable membership functions making it less vulnerable to initial design assumptions. Once the system is identified around an operation point, it is regulated to zero adaptively. Weight updating laws for the involved HONNFs are provided, which guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. The existence of the control signal is always assured by introducing a novel method of parameter hopping, which is incorporated in the weight updating law. Simulations illustrate the potency of the method and comparisons with conventional approaches on benchmarking systems are given. Also, the applicability of the method is tested on a direct current (dc) motor system where it is shown that by following the proposed procedure one can obtain asymptotic regulation.     

Colloidal Quantum Dot Infrared Lasers Featuring Sub-Single-Exciton Threshold and Very High Gain

The use of colloidal quantum dots (CQDs) as a gain medium in infrared laser devices has been underpinned by the need for high pumping intensities, very short gain lifetimes, and low gain coefficients. Here, PbS/PbSSe core/alloyed-shell CQDs are employed as an infrared gain medium that results in highly suppressed Auger recombination with a lifetime of 485 ps, lowering the amplified spontaneous emission (ASE) threshold down to 300 µJ cm⁻², and showing a record high net modal gain coefficient of 2180 cm⁻¹. By doping these engineered core/shell CQDs up to nearly filling the first excited state, a significant reduction of optical gain threshold is demonstrated, measured by transient absorption, to an average-exciton population-per-dot 〈N^th〉_g of 0.45 due to bleaching of the ground state absorption. This in turn have led to a fivefold reduction in ASE threshold at 〈N^th〉_ASE = 0.70 excitons-per-dot, associated with a gain lifetime of 280 ps. Finally, these heterostructured QDs are used to achieve near-infrared lasing at 1670 nm at a pump fluences corresponding to sub-single-exciton-per-dot threshold (〈N^th〉_Las = 0.87). This work brings infrared CQD lasing thresholds on par to their visible counterparts, and paves the way toward solution-processed infrared laser diodes.     

Customized kernel execution on reconfigurable hardware for embedded applications

To conserve space and power as well as to harness high performance in embedded systems, high utilization of the hardware is required. This can be facilitated through dynamic adaptation of the silicon resources in reconfigurable systems in order to realize various customized kernels as execution proceeds. Fortunately, the encountered reconfiguration overheads can be estimated. Therefore, if the scheduling of time-consuming kernels considers also the reconfiguration overheads, an overall performance gain can be obtained. We present our policy, experiments, and performance results of customizing and reconfiguring Field-Programmable Gate Arrays (FPGAs) for embedded kernels. Experiments involving EEMBC (EDN Embedded Microprocessor Benchmarking Consortium) and MiBench embedded benchmark kernels show high performance using our main policy, when considering reconfiguration overheads. Our policy reduces the required reconfigurations by more than 50% as compared to brute-force solutions, and performs within 25% of the ideal execution time while conserving 60% of the FPGA resources. Alternative strategies to reduce the reconfiguration overhead are also presented and evaluated.     

Behavioral adaptation of information systems through goal models

Customizing software to perfectly fit individual needs is becoming increasingly important in information systems engineering. Users want to be able to customize software behavior through reference to terms familiar to their diverse needs and experience. We present a requirements-driven approach to behavioral customization of software systems. Goal models are constructed to represent alternative behaviors that users can exhibit to achieve their goals. Customization information is then added to restrict the space of possibilities to those that fit specific users, contexts, or situations. Meanwhile, elements of the goal models are mapped to units of source code. This way, customization preferences posed at the requirements level are directly translated into system customizations. Our approach, which we apply to an on-line shopping cart system and an automated teller machine simulator, does not assume adoption of a particular development methodology, platform, or variability implementation technique and keeps the reasoning computation overhead from interfering with the execution of the configured application.