The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.

Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge

Homologous and unique G protein alpha subunits in the nematode Caenorhabditis elegans

A cDNA corresponding to a known G protein alpha subunit, the alpha subunit of Go (Go alpha), was isolated and sequenced. The predicted amino acid sequence of C. elegans Go alpha is 80-87% identical to other Go alpha sequences. An mRNA that hybridizes to the C. elegans Go alpha cDNA can be detected on Northern blots. A C. elegans protein that crossreacts with antibovine Go alpha antibody can be detected on immunoblots. A cosmid clone containing the C. elegans Go alpha gene (goa-1) was isolated and mapped to chromosome I. The genomic fragments of three other C. elegans G protein alpha subunit genes (gpa-1, gpa-2, and gpa-3) have been isolated using the polymerase chain reaction. The corresponding cosmid clones were isolated and mapped to disperse locations on chromosome V. The sequences of two of the genes, gpa-1 and gpa-3, were determined. The predicted amino acid sequences of gpa-1 and gpa-3 are only 48% identical to each other. Therefore, they are likely to have distinct functions. In addition they are not homologous enough to G protein alpha subunits in other organisms to be classified. Thus C. elegans has G proteins that are identifiable homologues of mammalian G proteins as well as G proteins that appear to be unique to C. elegans. Study of identifiable G proteins in C. elegans may result in a further understanding of their function in other organisms, whereas study of the novel G proteins may provide an understanding of unique aspects of nematode physiology

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

On large-scale diagonalization techniques for the Anderson model of localization

We propose efficient preconditioning algorithms for an eigenvalue problem arising in quantum physics, namely the computation of a few interior eigenvalues and their associated eigenvectors for large-scale sparse real and symmetric indefinite matrices of the Anderson model of localization. We compare the Lanczos algorithm in the 1987 implementation by Cullum and Willoughby with the shift-and-invert techniques in the implicitly restarted Lanczos method and in the Jacobi–Davidson method. Our preconditioning approaches for the shift-and-invert symmetric indefinite linear system are based on maximum weighted matchings and algebraic multilevel incomplete LDLT factorizations. These techniques can be seen as a complement to the alternative idea of using more complete pivoting techniques for the highly ill-conditioned symmetric indefinite Anderson matrices. We demonstrate the effectiveness and the numerical accuracy of these algorithms. Our numerical examples reveal that recent algebraic multilevel preconditioning solvers can accelerate the computation of a large-scale eigenvalue problem corresponding to the Anderson model of localization by several orders of magnitude

Sixth Annual Users' Conference

Conference papers and presentation outlines which address the use of the Transportable Applications Executive (TAE) and its various applications programs are compiled. Emphasis is given to the design of the user interface and image processing workstation in general. Alternate ports of TAE and TAE subsystems are also covered

Repositioning the Catalytic Triad Aspartic Acid of Haloalkane Dehalogenase: Effects on Stability, Kinetics, and Structure

Haloalkane dehalogenase (DhlA) catalyzes the hydrolysis of haloalkanes via an alkyl-enzyme intermediate. The covalent intermediate, which is formed by nucleophilic substitution with Asp124, is hydrolyzed by a water molecule that is activated by His289. The role of Asp260, which is the third member of the catalytic triad, was studied by site-directed mutagenesis. Mutation of Asp260 to asparagine resulted in a catalytically inactive D260N mutant, which demonstrates that the triad acid Asp260 is essential for dehalogenase activity. Furthermore, Asp260 has an important structural role, since the D260N enzyme accumulated mainly in inclusion bodies during expression, and neither substrate nor product could bind in the active-site cavity. Activity for brominated substrates was restored to D260N by replacing Asn148 with an aspartic or glutamic acid. Both double mutants D260N+N148D and D260N+N148E had a 10-fold reduced kcat and 40-fold higher Km values for 1,2-dibromoethane compared to the wild-type enzyme. Pre-steady-state kinetic analysis of the D260N+N148E double mutant showed that the decrease in kcat was mainly caused by a 220-fold reduction of the rate of carbon-bromine bond cleavage and a 10-fold decrease in the rate of hydrolysis of the alkyl-enzyme intermediate. On the other hand, bromide was released 12-fold faster and via a different pathway than in the wild-type enzyme. Molecular modeling of the mutant showed that Glu148 indeed could take over the interaction with His289 and that there was a change in charge distribution in the tunnel region that connects the active site with the solvent. On the basis of primary structure similarity between DhlA and other α/β-hydrolase fold dehalogenases, we propose that a conserved acidic residue at the equivalent position of Asn148 in DhlA is the third catalytic triad residue in the latter enzymes.

Bio-inspired call-stack reconstruction for performance analysis

The correlation of performance bottlenecks and their associated source code has become a cornerstone of performance analysis. It allows understanding why the efficiency of an application falls behind the computer's peak performance and enabling optimizations on the code ultimately. To this end, performance analysis tools collect the processor call-stack and then combine this information with measurements to allow the analyst comprehend the application behavior. Some tools modify the call-stack during run-time to diminish the collection expense but at the cost of resulting in non-portable solutions. In this paper, we present a novel portable approach to associate performance issues with their source code counterpart. To address it, we capture a reduced segment of the call-stack (up to three levels) and then process the segments using an algorithm inspired by multi-sequence alignment techniques. The results of our approach are easily mapped to detailed performance views, enabling the analyst to unveil the application behavior and its corresponding region of code. To demonstrate the usefulness of our approach, we have applied the algorithm to several first-time seen in-production applications to describe them finely, and optimize them by using tiny modifications based on the analyses.We thankfully acknowledge Mathis Bode for giving us access to the Arts CF binaries, and Miguel Castrillo and Kim Serradell for their valuable insight regarding Nemo. We would like to thank Forschungszentrum Jülich for the computation time on their Blue Gene/Q system. This research has been partially funded by the CICYT under contracts No. TIN2012-34557 and TIN2015-65316-P.Peer ReviewedPostprint (author's final draft