Does Baldrige Make a Business Case for Quality?

The Malcolm Baldrige National Quality Award (MBNQA) is a widely accepted model promoting quality management as a means to business success. However, because business results are themselves part of the model, the contribution of the approach-deployment elements to results cannot be determined. The National Institute of Standards and Technology (NIST) maintains a history of Baldrige applications and the results of their evaluations. Statistical analysis of these data could yield insight into whether the approach-deployment advocated by the Baldrige model actually produces excellent results. Although NIST does not currently allow access to the data, future empirical evaluation of the data could help determine the effectiveness of the Baldrige model and determine the relative degree of importance of each of the approach-deployment elements

Performance Degradation Analysis of Aviation Hydraulic Piston Pump Based on Mixed Wear Theory

This paper focuses on the mathematical modeling of axial piston pump through dividing the failure development of friction pair into lubrication, mixed lubrication and abrasion. Directing to the wedge-shaped oil film between cylinder block and valve plate, the support force distribution under the temperature variance was obtained. Considering the rough peak of valve plate, the contact load model is built under plastic deformation and elastic deformation and the corresponding wear volume is calculated. Computing the wear and tear along the counter-clockwise, the total amount of friction and wear can be calculated. Simulation and preliminary wear particle monitoring test indicates that proposed modeling and analysis can effectively reflect the real abrasion process of hydraulic piston pump. © 2017 Published by Faculty of Engineerin

Secondary Electron Yield of Electron Beam Welded Areas of SRF Cavities

Secondary Electron Emission (SEE) is a phenomenon that contributes to the total electron activity inside the Superconducting Radiofrequency (SRF) cavities during the accelerator operation. SEE is highly dependent on the state of the surface. During electron beam welding process, significant amount of heat is introduced into the material causing the microstructure change of Niobium (Nb). Currently, all simulation codes for field emission and multipacting are treating the inside of the cavity as a uniform, homogeneous surface. Due to its complex shape and fabricating procedure, and the sensitivity of the SEE on the surface state, it would be interesting to see if the Secondary Electron Yield (SEY) parameters vary in the surface area on and near the equator weld. For that purpose, we have developed experimental setup that can measure accurately the energy distribution of the SEY of coupon-like like samples. To test the influence of the weld area on the SEY of Nb, dedicated samples are made from a welded plate using electron beam welding parameters common for cavity fabrication. SEY data matrix of those samples will be presented

Effects of Plasma Processing on Secondary Electron Yield of Niobium Samples

Impurities deposited on the surface of Nb during both the forming and welding of accelerator cavities add to the imperfections of the sheet metal, which then affects the overall performance of the cavities. This leads to a drop in the Q factor and limits the maximum acceleration gradient achievable per unit length of the cavities. The performance can be improved either by adjusting the fabrication and preparation parameters, or by mitigating the effects of fabrication and preparation techniques used. We have developed the experimental setup to determine Secondary Electron Yield (SEY) from the surface of Nb samples. Our aim is to show the effect of plasma processing on the SEY of Nb. The setup measures the secondary electron energy distribution at various incident angles as measured between the electron beam and the surface of the sample. The goal is to determine the SEY on non-treated and plasma treated surface of electron beam welded samples. Here we describe the experimental setup, plasma treatment device, and fabrication and processing of the Nb samples

Pilot Programs for Veterans Transition To Engineering Fields

Veterans, through their active service, frequently receive training in highly skilled technical areas. However, they may lack a theoretical background in underlying engineering principles. They also need additional support with the transition from a highly structured military environment to an environment with more ambiguous time constraints and different sorts of responsibilities. Moreover they are facing challenges which are specific for their student population. Therefore, enabling multiple mechanisms which would support them and provide them necessary guidance are especially important at universities with large veteran populations such as at Old Dominion University in Norfolk, Virginia. Hence, there is a need for programs which build on the specialized training that veterans received and aid in their academic journey. This paper will introduce three pilot programs for advancing engineering education for military veterans focusing on forming a support base for veterans to assist them in overcoming traditional educational barriers

Transcription factor site dependencies in human, mouse and rat genomes

<p>Abstract</p> <p>Background</p> <p>It is known that transcription factors frequently act together to regulate gene expression in eukaryotes. In this paper we describe a computational analysis of transcription factor site dependencies in human, mouse and rat genomes.</p> <p>Results</p> <p>Our approach for quantifying tendencies of transcription factor binding sites to co-occur is based on a binding site scoring function which incorporates dependencies between positions, the use of information about the structural class of each transcription factor (major/minor groove binder), and also considered the possible implications of varying GC content of the sequences. Significant tendencies (dependencies) have been detected by non-parametric statistical methodology (permutation tests). Evaluation of obtained results has been performed in several ways: reports from literature (many of the significant dependencies between transcription factors have previously been confirmed experimentally); dependencies between transcription factors are not biased due to similarities in their DNA-binding sites; the number of dependent transcription factors that belong to the same functional and structural class is significantly higher than would be expected by chance; supporting evidence from GO clustering of targeting genes. Based on dependencies between two transcription factor binding sites (second-order dependencies), it is possible to construct higher-order dependencies (networks). Moreover results about transcription factor binding sites dependencies can be used for prediction of groups of dependent transcription factors on a given promoter sequence. Our results, as well as a scanning tool for predicting groups of dependent transcription factors binding sites are available on the Internet.</p> <p>Conclusion</p> <p>We show that the computational analysis of transcription factor site dependencies is a valuable complement to experimental approaches for discovering transcription regulatory interactions and networks. Scanning promoter sequences with dependent groups of transcription factor binding sites improve the quality of transcription factor predictions.</p

An intuitionistic approach to scoring DNA sequences against transcription factor binding site motifs

Background: Transcription factors (TFs) control transcription by binding to specific regions of DNA called transcription factor binding sites (TFBSs). The identification of TFBSs is a crucial problem in computational biology and includes the subtask of predicting the location of known TFBS motifs in a given DNA sequence. It has previously been shown that, when scoring matches to known TFBS motifs, interdependencies between positions within a motif should be taken into account. However, this remains a challenging task owing to the fact that sequences similar to those of known TFBSs can occur by chance with a relatively high frequency. Here we present a new method for matching sequences to TFBS motifs based on intuitionistic fuzzy sets (IFS) theory, an approach that has been shown to be particularly appropriate for tackling problems that embody a high degree of uncertainty. Results: We propose SCintuit, a new scoring method for measuring sequence-motif affinity based on IFS theory. Unlike existing methods that consider dependencies between positions, SCintuit is designed to prevent overestimation of less conserved positions of TFBSs. For a given pair of bases, SCintuit is computed not only as a function of their combined probability of occurrence, but also taking into account the individual importance of each single base at its corresponding position. We used SCintuit to identify known TFBSs in DNA sequences. Our method provides excellent results when dealing with both synthetic and real data, outperforming the sensitivity and the specificity of two existing methods in all the experiments we performed. Conclusions: The results show that SCintuit improves the prediction quality for TFs of the existing approaches without compromising sensitivity. In addition, we show how SCintuit can be successfully applied to real research problems. In this study the reliability of the IFS theory for motif discovery tasks is proven

N-gram analysis of 970 microbial organisms reveals presence of biological language models

<p>Abstract</p> <p>Background</p> <p>It has been suggested previously that genome and proteome sequences show characteristics typical of natural-language texts such as "signature-style" word usage indicative of authors or topics, and that the algorithms originally developed for natural language processing may therefore be applied to genome sequences to draw biologically relevant conclusions. Following this approach of 'biological language modeling', statistical n-gram analysis has been applied for comparative analysis of whole proteome sequences of 44 organisms. It has been shown that a few particular amino acid n-grams are found in abundance in one organism but occurring very rarely in other organisms, thereby serving as genome signatures. At that time proteomes of only 44 organisms were available, thereby limiting the generalization of this hypothesis. Today nearly 1,000 genome sequences and corresponding translated sequences are available, making it feasible to test the existence of biological language models over the evolutionary tree.</p> <p>Results</p> <p>We studied whole proteome sequences of 970 microbial organisms using n-gram frequencies and cross-perplexity employing the Biological Language Modeling Toolkit and Patternix Revelio toolkit. Genus-specific signatures were observed even in a simple unigram distribution. By taking statistical n-gram model of one organism as reference and computing cross-perplexity of all other microbial proteomes with it, cross-perplexity was found to be predictive of branch distance of the phylogenetic tree. For example, a 4-gram model from proteome of <it>Shigellae flexneri 2a</it>, which belongs to the <it>Gammaproteobacteria </it>class showed a self-perplexity of 15.34 while the cross-perplexity of other organisms was in the range of 15.59 to 29.5 and was proportional to their branching distance in the evolutionary tree from <it>S. flexneri</it>. The organisms of this genus, which happen to be pathotypes of <it>E.coli</it>, also have the closest perplexity values with <it>E. coli.</it></p> <p>Conclusion</p> <p>Whole proteome sequences of microbial organisms have been shown to contain particular n-gram sequences in abundance in one organism but occurring very rarely in other organisms, thereby serving as proteome signatures. Further it has also been shown that perplexity, a statistical measure of similarity of n-gram composition, can be used to predict evolutionary distance within a genus in the phylogenetic tree.</p

A Feature-Based Approach to Modeling Protein–DNA Interactions

Transcription factor (TF) binding to its DNA target site is a fundamental regulatory interaction. The most common model used to represent TF binding specificities is a position specific scoring matrix (PSSM), which assumes independence between binding positions. However, in many cases, this simplifying assumption does not hold. Here, we present feature motif models (FMMs), a novel probabilistic method for modeling TF–DNA interactions, based on log-linear models. Our approach uses sequence features to represent TF binding specificities, where each feature may span multiple positions. We develop the mathematical formulation of our model and devise an algorithm for learning its structural features from binding site data. We also developed a discriminative motif finder, which discovers de novo FMMs that are enriched in target sets of sequences compared to background sets. We evaluate our approach on synthetic data and on the widely used TF chromatin immunoprecipitation (ChIP) dataset of Harbison et al. We then apply our algorithm to high-throughput TF ChIP data from mouse and human, reveal sequence features that are present in the binding specificities of mouse and human TFs, and show that FMMs explain TF binding significantly better than PSSMs. Our FMM learning and motif finder software are available at http://genie.weizmann.ac.il/

Computational Structural Analysis: Multiple Proteins Bound to DNA

BACKGROUND: With increasing numbers of crystal structures of proteinratioDNA and proteinratioproteinratioDNA complexes publically available, it is now possible to extract sufficient structural, physical-chemical and thermodynamic parameters to make general observations and predictions about their interactions. In particular, the properties of macromolecular assemblies of multiple proteins bound to DNA have not previously been investigated in detail. METHODOLOGY/PRINCIPAL FINDINGS: We have performed computational structural analyses on macromolecular assemblies of multiple proteins bound to DNA using a variety of different computational tools: PISA; PROMOTIF; X3DNA; ReadOut; DDNA and DCOMPLEX. Additionally, we have developed and employed an algorithm for approximate collision detection and overlapping volume estimation of two macromolecules. An implementation of this algorithm is available at http://promoterplot.fmi.ch/Collision1/. The results obtained are compared with structural, physical-chemical and thermodynamic parameters from proteinratioprotein and single proteinratioDNA complexes. Many of interface properties of multiple proteinratioDNA complexes were found to be very similar to those observed in binary proteinratioDNA and proteinratioprotein complexes. However, the conformational change of the DNA upon protein binding is significantly higher when multiple proteins bind to it than is observed when single proteins bind. The water mediated contacts are less important (found in less quantity) between the interfaces of components in ternary (proteinratioproteinratioDNA) complexes than in those of binary complexes (proteinratioprotein and proteinratioDNA).The thermodynamic stability of ternary complexes is also higher than in the binary interactions. Greater specificity and affinity of multiple proteins binding to DNA in comparison with binary protein-DNA interactions were observed. However, protein-protein binding affinities are stronger in complexes without the presence of DNA. CONCLUSIONS/SIGNIFICANCE: Our results indicate that the interface properties: interface area; number of interface residues/atoms and hydrogen bonds; and the distribution of interface residues, hydrogen bonds, van der Walls contacts and secondary structure motifs are independent of whether or not a protein is in a binary or ternary complex with DNA. However, changes in the shape of the DNA reduce the off-rate of the proteins which greatly enhances the stability and specificity of ternary complexes compared to binary ones