Responding to God\u27s Guidance

Interview by Joshua Shuart and Laurence Weinstein of Craig Hoekenga, CEO of Microboard Processing, Inc. Craig Hoekenga is CEO of Microboard Processing, Inc. (MPI), a very successful subcontract electronics manufacturing company located in Seymour, Connecticut. There are many reasons why Hoekenga would stand out in any gathering of CEOs, but one of the most arresting reasons would be that Hoekenga credits his success to God and considers MPI a “Christian business.” The New England Journal of Entrepreneurhip editors started with a plant tour and then caught up with Hoekenga in his office

Mind over machine : what Deep Blue taught us about chess, artificial intelligence, and the human spirit

Thesis (S.M. in Science Writing)--Massachusetts Institute of Technology, Dept. of Humanities, Graduate Program in Science Writing, 2007."September 2007."Includes bibliographical references (leaves 44-49).On May 11th 1997, the world watched as IBM's chess-playing computer Deep Blue defeated world chess champion Garry Kasparov in a six-game match. The reverberations of that contest touched people, and computers, around the world. At the time, it was difficult to assess the historical significance of the moment, but ten years after the fact, we can take a fresh look at the meaning of the computer's victory. With hindsight, we can see how Deep Blue impacted the chess community and influenced the fields of philosophy, artificial intelligence, and computer science in the long run. For the average person, Deep Blue embodied many of our misgivings about computers becoming our new partners in the information age. For researchers in the field it was emblematic of the growing pains experienced by the evolving field of AI over the previous half century. In the end, what might have seemed like a definitive, earth-shattering event was really the next step in our on-going journey toward understanding mind and machine. While Deep Blue was a milestone - the end of a long struggle to build a masterful chess machine - it was also a jumping off point for other lines of inquiry from new supercomputing projects to the further development of programs that play other games, such as Go. Ultimately, the lesson of Deep Blue's victory is that we will continue to accomplish technological feats we thought impossible just a few decades before. And as we reach each new goalpost, we will acclimate to our new position, recognize the next set of challenges before us, and push on toward the next target.by Barbara Christine Hoekenga.S.M.in Science Writin

Identification and Characterization of Aluminum Tolerance Loci in Arabidopsis (Landsberg erecta x Columbia) by Quantitative Trait Locus Mapping. A Physiologically Simple But Genetically Complex Trait

Aluminum (Al) toxicity, which is caused by the solubilization of Al3+ in acid soils resulting in inhibition of root growth and nutrient/water acquisition, is a serious limitation to crop production, because up to one-half of the world's potentially arable land is acidic. To date, however, no Al tolerance genes have yet been cloned. The physiological mechanisms of tolerance are somewhat better understood; the major documented mechanism involves the Al-activated release of Al-binding organic acids from the root tip, preventing uptake into the primary site of toxicity. In this study, a quantitative trait loci analysis of Al tolerance in Arabidopsis was conducted, which also correlated Al tolerance quantitative trait locus (QTL) with physiological mechanisms of tolerance. The analysis identified two major loci, which explain approximately 40% of the variance in Al tolerance observed among recombinant inbred lines derived from Landsberg erecta (sensitive) and Columbia (tolerant). We characterized the mechanism by which tolerance is achieved, and we found that the two QTL cosegregate with an Al-activated release of malate from Arabidopsis roots. Although only two of the QTL have been identified, malate release explains nearly all (95%) of the variation in Al tolerance in this population. Al tolerance in Landsberg erecta × Columbia is more complex genetically than physiologically, in that a number of genes underlie a single physiological mechanism involving root malate release. These findings have set the stage for the subsequent cloning of the genes responsible for the Al tolerance QTL, and a genomics-based cloning strategy and initial progress on this are also discussed

Single-kernel ionomic profiles are highly heritable indicators of genetic and environmental influences on elemental accumulation in maize grain (Zea mays)

Peer reviewedPublisher PD

Weighted Correlation Network Analysis (WGCNA) Applied to the Tomato Fruit Metabolome

BACKGROUND: Advances in "omics" technologies have revolutionized the collection of biological data. A matching revolution in our understanding of biological systems, however, will only be realized when similar advances are made in informatic analysis of the resulting "big data." Here, we compare the capabilities of three conventional and novel statistical approaches to summarize and decipher the tomato metabolome. METHODOLOGY: Principal component analysis (PCA), batch learning self-organizing maps (BL-SOM) and weighted gene co-expression network analysis (WGCNA) were applied to a multivariate NMR dataset collected from developmentally staged tomato fruits belonging to several genotypes. While PCA and BL-SOM are appropriate and commonly used methods, WGCNA holds several advantages in the analysis of highly multivariate, complex data. CONCLUSIONS: PCA separated the two major genetic backgrounds (AC and NC), but provided little further information. Both BL-SOM and WGCNA clustered metabolites by expression, but WGCNA additionally defined "modules" of co-expressed metabolites explicitly and provided additional network statistics that described the systems properties of the tomato metabolic network. Our first application of WGCNA to tomato metabolomics data identified three major modules of metabolites that were associated with ripening-related traits and genetic background

Applying network and genetic analysis to the potato metabolome

Compositional traits in potato [Solanum tuberosum L.] are economically important but genetically complex, often controlled by many loci of small effect; new methods need to be developed to accelerate analysis and improvement of such traits, like chip quality. In this study, we used network analysis to organize hundreds of metabolic features detected by mass spectrometry into groups, as a precursor to genetic analysis. 981 features were condensed into 44 modules; module eigenvalues were used for genetic mapping and correlation analysis with phenotype data collected by the Solanaceae Coordinated Agricultural Project. Half of the modules were associated with at least one SNP according to GWAS; 11 of those modules were also significantly correlated with chip color. Within those modules features associated with chipping provide potential targets for selection in addition to selection for reduced glucose. Loci associated with module eigenvalues were not evenly distributed throughout the genome but were instead clustered on chromosomes 3, 7, and 8. Comparison of GWAS on single features and modules of clustered features often identified the same SNPs. However, features with related chemistries (for example, glycoalkaloids with precursor/product relationships) were not found to be near neighbors in the network analysis and did not share common SNPs from GWAS. Instead, the features within modules were often structurally disparate, suggesting that linkage disequilibrium complicates network analyses in potato. This result is consistent with recent genomic studies of potato showing that chromosomal rearrangements that create barriers to recombination are common in cultivated germplasm