Self-organizing maps for pattern recognition in design of alloys

A combined experimental–computational methodology for accelerated design of AlNiCo-type permanent magnetic alloys is presented with the objective of simultaneously extremizing several magnetic properties. Chemical concentrations of eight alloying elements were initially generated using a quasi-random number generator so as to achieve a uniform distribution in the design variable space. It was followed by manufacture and experimental evaluation of these alloys using an identical thermo-magnetic protocol. These experimental data were used to develop meta-models capable of directly relating the chemical composition with desired macroscopic properties of the alloys. These properties were simultaneously optimized to predict chemical compositions that result in improvement of properties. These data were further utilized to discover various correlations within the experimental dataset by using several concepts of artificial intelligence. In this work, an unsupervised neural network known as self-organizing maps was used to discover various patterns reported in the literature. These maps were also used to screen the composition of the next set of alloys to be manufactured and tested in the next iterative cycle. Several of these Pareto-optimized predictions out-performed the initial batch of alloys. This approach helps significantly reducing the time and the number of alloys needed in the alloy development process.     

Isolation, characterization, and partial purification of a novel ubiquitin-protein ligase, E3. Targeting of protein substrates via multiple and distinct recognition signals and conjugating enzymes

Degradation of a protein via the ubiquitin system involves two discrete steps, conjugation of ubiquitin to the substrate and degradation of the adduct. Conjugation follows a three-step mechanism. First, ubiquitin is activated by the ubiquitin-activating enzyme, E1. Following activation, one of several E2 enzymes (ubiquitin-carrier proteins or ubiquitin-conjugating enzymes, UBCs) transfers ubiquitin from E1 to the protein substrate that is bound to one of several ubiquitin-protein ligases, E3s. These enzymes catalyze the last step in the process, covalent attachment of ubiquitin to the protein substrate. The binding of the substrate to E3 is specific and implies that E3s play a major role in recognition and selection of proteins for conjugation and subsequent degradation. So far, only a few ligases have been identified, and it is clear that many more have not been discovered yet. Here, we describe a novel ligase that is involved in the conjugation and degradation of non "N-end rule" protein substrates such as actin, troponin T, and MyoD. This substrate specificity suggests that the enzyme may be involved in degradation of muscle proteins. The ligase acts in concert with E2-F1, a previously described non N-end rule UBC. Interestingly, it is also involved in targeting lysozyme, a bona fide N-end substrate that is recognized by E3 alpha and E2-14 kDa. The novel ligase recognizes lysozyme via a signal(s) that is distinct from the N-terminal residue of the protein. Thus, it appears that certain proteins can be targeted via multiple recognition motifs and distinct pairs of conjugating enzymes. We have purified the ligase approximately 200-fold and demonstrated that it is different from other known E3s, including E3 alpha/UBR1, E3 beta, and E6-AP. The native enzyme has an apparent molecular mass of approximately 550 kDa and appears to be a homodimer. Because of its unusual size, we designated this novel ligase E3L (large). E3L contains an -SH group that is essential for its activity. Like several recently described E3 enzymes, including E6-AP and the ligase involved in the processing of p105, the NF-kappa B precursor, the novel ligase is found in mammalian tissues but not in wheat germ.     

2,4-Diamino-5-(2'-arylpropargyl)pyrimidine derivatives as new nonclassical antifolates for human dihydrofolate reductase inhibition

Dihydrofolate reductase (DHFR) has been a well-recognized target for the development of therapeutics for human cancers for several decades. Classical inhibitors of DHFR use an active transport mechanism to gain access to the cell; disabling this mechanism creates a pathway for resistance. In response, recent research focuses on nonclassical lipid-soluble DHFR inhibitors that are designed to passively diffuse through the membrane. Here, a new series of propargyl-linked antifolates are investigated as potential nonclassical human DHFR inhibitors. Several of these compounds exhibit potent enzyme inhibition with 50% inhibition concentration values under 500 nM. Molecular docking investigations show that the compounds maintain conserved hydrogen bonds between the pyrimidine ring and the enzyme as well as form van der Waals interactions with critical residues in the active site. Interestingly, the most potent compound, 2,4-diamino-5-(3-(3,4,5-trimethoxyphenyl)prop-1-ynyl)-6-ethylpyrimidine (compound 35), is 3500-fold more potent than trimethoprim, a potent inhibitor of bacterial DHFR but weak inhibitor of human DHFR. The two structural differences between compound 35 and trimethoprim show that the propargyl linkage and the substitution at C6 of the pyrimidine ring are critical to the formation of contacts with Thr 56, Ser 59, Ile 60, Leu 22, Phe 31 and Phe 34 and hence, to enhancing potency. The propargyl-linked antifolates are efficient ligands with a high ratio of potency to the number of non-hydrogen atoms and represent a potentially fruitful avenue for future development of antineoplastic agents.     

New Generations: Sequencing Machines and Their Computational Challenges

New generation sequencing systems are changing how molecular biology is practiced. The widely promoted ＄1000 genome will be a reality with attendant changes for healthcare, including personalized medicine. More broadly the genomes of many new organisms with large samplings from populations will be commonplace. What is less appreciated is the explosive demands on computation, both for CPU cycles and storage as well as the need for new computational methods. In this article we will survey some of these develo...     

Linking air quality and watershed models for environmental assessments: Analysis of the effects of model-specific precipitation estimates on calculated water flux

Directly linking air quality and watershed models could provide an effective method for estimating spatially-explicit inputs of atmospheric contaminants to watershed biogeochemical models. However, to adequately link air and watershed models for wet deposition estimates, each model’s temporal and spatial representation of precipitation needs to be consistent. We explore how precipitation implemented within the Community Multi-Scale Air Quality Model (CMAQ) model algorithms, and multiple spatially-explicit precipitation datasets that could be used to improve the CMAQ model deposition estimates, links with the standard precipitation sources used to calibrate watershed models (i.e., rain gage data) via modeled water fluxes. Simulations are run using a grid-based watershed mercury model (GBMM) in two watersheds. Modeled monthly runoff suggests that multiple resolution Parameter-elevations Regressions on Independent Slopes Model (PRISM) and National Multi-sensor Precipitation Analysis Stage IV (NPA) data generate similar monthly runoff estimates, with comparable or greater accuracy when evaluated against stream gage data than that produced by the base rain gage data. However, across longer time periods, simulated water balances using 36 km Pennsylvania State University/National Center for Atmospheric Research mesoscale model (MM5) data are similar to that of base data. The investigation also examines the implications our results, providing suggestions for linking air quality and watershed fate and transport models.     

<Emphasis Type="Italic">A Midsummer Night’s Dream</Emphasis> (with flying robots)

Seven flying robot “fairies” joined human actors in the Texas A&;M production of William Shakespeare’s A Midsummer Night’s Dream. The production was a collaboration between the departments of Computer Science and Engineering, Electrical and Computer Engineering, and Theater Arts. The collaboration was motivated by two assertions. First, that the performing arts have principles for creating believable agents that will transfer to robots. Second, the theater is a natural testbed for evaluating the response of untrained human groups (both actors and the audience) to robots interacting with humans in shared spaces, i.e., were believable agents created? The production used two types of unmanned aerial vehicles, an AirRobot 100-b quadrotor platform about the size of a large pizza pan, and six E-flite Blade MCX palm-sized toy helicopters. The robots were used as alter egos for fairies in the play; the robots did not replace any actors, instead they were paired with them. The insertion of robots into the production was not widely advertised so the audience was the typical theatergoing demographic, not one consisting of people solely interested technology. The use of radio-controlled unmanned aerial vehicles provides insights into what types of autonomy are needed to create appropriate affective interactions with untrained human groups. The observations from the four weeks of practice and eight performances contribute (1) a taxonomy and methods for creating affect exchanges between robots and untrained human groups, (2) the importance of improvisation within robot theater, (3) insights into how untrained human groups form expectations about robots, and (4) awareness of the importance of safety and reliability as a design constraint for public engagement with robot platforms. The taxonomy captures that apparent affect can be created without explicit affective behaviors by the robot, but requires talented actors to convey the situation or express reactions. The audience’s response to robot crashes was a function of whether they had the opportunity to observe how the actors reacted to robot crashes on stage, suggesting that pre-existing expectations must be taken into account in the design of autonomy. Furthermore, it appears that the public expect robots to be more reliable (an expectation of consumer product hardening) and safe (an expectation from product liability) than the current capabilities and this may be a major challenge or even legal barrier for introducing robots into shared public spaces. These contributions are expected to inform design strategies for increasing public engagement with robot platforms through affect, and shows the value of arts-based approaches to public encounters with robots both for generating design strategies and for evaluation.     

Deriving industrial logistics hub reference models for manufacturing based economies

Global enterprises require extended logistics operations that integrate channel intermediaries into the network. The method for deriving integrated models for logistics hubs that improve the efficiency of manufacturers’ global operations is the focus of this paper. Under government sponsorship, six industrial logistics hubs were implemented by leading manufacturing sectors across Taiwan over a five year period from 2004 until 2008. Each industry sector had unique industry characteristics, business strategies, and logistics models. Therefore, this paper describes how generalized and quick to implement integrated logistics hubs are developed by studying the successful reference models and systems used by six independent industrial sectors. The research results provide a field tested method for deriving integrated logistics hub models in different manufacturing economies with notes that provide sufficient methodological detail for repeating the construction of logistics hubs in other manufacturing economies.     

Comparing measures of model selection for penalized splines in Cox models

This article presents an application and a simulation study of model fit criteria for selecting the optimal degree of smoothness for penalized splines in Cox models. The criteria considered were the Akaike information criterion, the corrected AIC, two formulations of the Bayesian information criterion, and a generalized cross-validation method. The estimated curves selected by the five methods were compared to each other in a study of rectal cancer mortality in autoworkers. In the stimulation study, we estimated the fit of the penalized spline models in six exposure-response scenarios, using the five model fit criteria. The methods were compared on the basis of a mean squared error score and the power and size of hypothesis tests for any effect and for detecting nonlinearity. All comparisons were made across a range in the total sample size and number of cases.     

A computer-enabled solution procedure for food wholesalers’ distribution decision in cities with a circular transportation infrastructure

This paper addresses the food distribution decision from a wholesaler's perspective, as the wholesalers are often faced with thorny issues such as when, where, how to deliver products at the lowest costs to best satisfy the retailers’ changing needs. This research relies on a distribution problem encountered at Northern Grocery Company in Beijing as an example to develop a two-stage solution procedure and subsequent computer-enabled programs for identifying efficient vehicle routing alternatives. The solution process, which integrates heuristic search algorithms and an integer programming model, can be applied to a wide range of distribution problems with specified route durations and a circular transportation network structure as exemplified by City of Beijing.