Transducin-alpha C-terminal peptide binding site consists of C-D and E-F loops of rhodopsin

The binding of heterotrimeric GTP-binding proteins (G-proteins) to serpentine receptors involves several independent contacts. We have deduced the points of interaction between mutant bovine rhodopsins and alphat-(340-350), a peptide corresponding to the C terminus of the alpha subunit (alphat) of bovine retinal G-protein, transducin. Direct binding of alphat-(340-350) to rhodopsin stabilizes the activated metarhodopsin II state (M II), consequently uncoupling the rhodopsin-transducin interaction. This peptide action requires two segments on the cytoplasmic domain of rhodopsin: the Tyr136-Val137-Val138-Val139 sequence on the C-D loop and the Glu247-Lys248-Glu249-Val250-Thr251 sequence on the E-F loop. We propose that a tertiary interaction of these two loop regions forms a pocket for binding the alphat C terminus of the transducin during light transduction in vivo. In most G-proteins, the C termini of alpha subunits are important for interaction with receptors, and, in several serpentine receptors, regions similar to those in rhodopsin are essential for G-protein activation, indicating that the interaction described here may be a generally applicable mode of G-protein binding in signal transduction.     

Machining feature-based similarity assessment algorithms for prismatic machined parts

This paper presents algorithms for identifying machined parts in a database that are similar to a given query part based on machining features. In this paper we only consider parts that are machined on 3-axis machining centers. We utilize reduced feature vectors consisting of machining feature access directions, feature types, feature volumes, feature dimensional tolerances and feature group cardinality as a basis for assessing shape similarity. We have defined a distance function between two sets of reduced feature vectors to assess the similarity between them from the machining effort point of view. To assess the similarity between the two parts, one set of reduced feature vectors is transformed in space using rigid body transformations with respect to the other set such that the distance between them is minimized. The distance between the two sets of aligned reduced feature vectors is used as a measure of similarity between the two parts. The existing machined parts are rank ordered based on the value of the distance with respect to the query part. The cost of previously machined parts that have a very small distance from the query part can be used as a basis for estimating the cost of machining the new part.     

Monitoring task loading with multivariate EEG measures during complex forms of human-computer interaction

Electroencephalographic (EEG) recordings were made while 16 participants performed versions of a personal-computer-based flight simulation task of low, moderate, or high difficulty. As task difficulty increased, frontal midline theta EEG activity increased and alpha band activity decreased. A participant-specific function that combined multiple EEG features to create a single load index was derived from a sample of each participant's data and then applied to new test data from that participant. Index values were computed for every 4 s of task data. Across participants, mean task load index values increased systematically with increasing task difficulty and differed significantly between the different task versions. Actual or potential applications of this research include the use of multivariate EEG-based methods to monitor task loading during naturalistic computer-based work.     

A 233-MHz 80%-87% efficient four-phase DC-DC converter utilizing air-core inductors on package

We demonstrate an integrated buck dc-dc converter for multi-V/sub CC/ microprocessors. At nominal conditions, the converter produces a 0.9-V output from a 1.2-V input. The circuit was implemented in a 90-nm CMOS technology. By operating at high switching frequency of 100 to 317 MHz with four-phase topology and fast hysteretic control, we reduced inductor and capacitor sizes by three orders of magnitude compared to previously published dc-dc converters. This eliminated the need for the inductor magnetic core and enabled integration of the output decoupling capacitor on-chip. The converter achieves 80%-87% efficiency and 10% peak-to-peak output noise for a 0.3-A output current and 2.5-nF decoupling capacitance. A forward body bias of 500 mV applied to PMOS transistors in the bridge improves efficiency by 0.5%-1%.     

Performance comparison of conventional and wiper ceramic inserts in hard turning through artificial neural network modeling

Hard turning with ceramic tools provides an alternative to grinding operation in machining high precision and hardened components. But, the main concerns are the cost of expensive tool materials and the effect of the process on machinability. The poor selection of cutting conditions may lead to excessive tool wear and increased surface roughness of workpiece. Hence, there is a need to investigate the effects of process parameters on machinability characteristics in hard turning. In this work, the influence of cutting speed, feed rate, and machining time on machinability aspects such as specific cutting force, surface roughness, and tool wear in AISI D2 cold work tool steel hard turning with three different ceramic inserts, namely, CC650, CC650WG, and GC6050WH has been studied. A multilayer feed-forward artificial neural network (ANN), trained using error back-propagation training algorithm has been employed for predicting the machinability. The input?Coutput patterns required for ANN training and testing are obtained from the turning experiments planned through full factorial design. The simulation results demonstrate the effectiveness of ANN models to analyze the effects of cutting conditions as well as to study the performance of conventional and wiper ceramic inserts on machinability.     

Distributed Optimal Self-Organization in Ad Hoc Wireless Sensor Networks

This work is motivated by the idea of using randomly deployed wireless networks of miniature smart sensors to serve as distributed instrumentation. In such applications, often the objective of the sensor network is to repeatedly compute and, if required, deliver to an observer some result based on the values measured at the sensors. We argue that in such applications it is important for the sensors to self-organize in a way that optimizes network throughput. We identify and discuss two main problems of optimal self-organization: (1) building an optimal topology, and (2) tuning network access parameters, such as the transmission attempt rate. We consider a simple random access model for sensor networks and formulate these problems as optimization problems. We then present centralized as well as distributed algorithms for solving them. Results show that the performance improvement is substantial and implementation of such optimal self-organization techniques may be worth the additional complexity.     

Fabrication of catalytic membranes for the treatment of drinking water using combined ozonation and ultrafiltration

The removal of disinfection byproducts and their precursors was investigated using a combined ozonation-ultrafiltration system. A commercial membrane was coated 20 or 40 times with iron oxide nanoparticles (4-6 nm in diameter). With this membrane, the concentration of dissolved organic carbon was reduced by >85% and the concentrations of simulated distribution system total trihalomethanes and simulated distribution system halo acetic acids decreased by up to 90% and 85%, respectively. When the coated membrane was used, the concentrations of aldehydes, ketones, and ketoacids in the permeate were reduced by >50% as compared to that obtained with the uncoated membranes. Hydroxyl or other radicals produced at the iron oxide coated membrane surface as a result of ozone decomposition are believed to have enhanced the degradation of the natural organic matter, thereby reducing the concentration of disinfection byproducts. While increasing the number of times the membrane was coated from 20 to 40 did not significantly reduce the concentrations of most of the parameters measured, it did result in a significant decrease in the concentrations of ozonation byproducts. Increasing the sintering temperature from 500 to 900 degrees C also resulted in an improvement in the removal of the ozonation byproducts.     

Prediction and Minimization of Delamination in Drilling of Medium-Density Fiberboard (MDF) Using Response Surface Methodology and Taguchi Design

In this article, an attempt has been made to predict and minimize the delamination in drilling of medium density fiberboard (MDF). The experiments are carried out on LAMIPAN PB panel based on orthogonal array with feed rate and cutting speed as process parameters. The second order delamination factor models at entry and exit of the holes have been developed using response surface methodology. The parametric analysis has been carried out to study the interaction effects of the machining parameters. Taguchi's quality loss function approach has been employed to simultaneously minimize the delamination factor at entry and exit of the holes. From the analysis of means and analysis of variance, the optimal combination level and the significant parameters on delamination factor are obtained. The optimization results showed that the combination of low feed rate with high cutting speed is necessary to minimize delamination in drilling of MDF.     

Microfluidic platform for controlled synthesis of polymeric nanoparticles

A central challenge in the development of drug-encapsulated polymeric nanoparticles is the inability to control the mixing processes required for their synthesis resulting in variable nanoparticle physicochemical properties. Nanoparticles may be developed by mixing and nanoprecipitation of polymers and drugs dissolved in organic solvents with nonsolvents. We used rapid and tunable mixing through hydrodynamic flow focusing in microfluidic channels to control nanoprecipitation of poly(lactic- co-glycolic acid)- b-poly(ethylene glycol) diblock copolymers as a model polymeric biomaterial for drug delivery. We demonstrate that by varying (1) flow rates, (2) polymer composition, and (3) polymer concentration we can optimize the size, improve polydispersity, and control drug loading and release of the resulting nanoparticles. This work suggests that microfluidics may find applications for the development and optimization of polymeric nanoparticles in the newly emerging field of nanomedicine.     

Diffusion-limited patterning of molecules in nanofluidic channels

Diffusion-limited patterning (DLP) is a new technique that enables patterning of labile molecular species in solution phase onto surfaces that are not easily accessible. This technique is self-aligning and is simple to implement for patterning multiple species. We demonstrated DLP by patterning alternating bands of fluorescently labeled and unlabeled streptavidin in biotin-functionalized nanofluidic channels with spatial resolution better than 1 microm. The methodology of DLP also enables experimental measurement of a unique parameter that relates molecular surface grafting density, concentration, diffusivity, and channel geometry.