Factory optima: a web-based system for composition and analysis of manufacturing service networks based on a reusable model repository.

This paper reports on the development of Factory Optima, a web-based system that allows manufacturing process engineers to compose, optimise and perform trade-off analysis of manufacturing and contract service networks based on a reusable repository of performance models. Performance models formally describe process feasibility constraints and metrics of interest, such as cost, throughput and CO 2 emissions, as a function of fixed and control parameters, such as equipment and contract properties and settings. The repository contains performance models representing (1) unit manufacturing processes, (2) base contract services and (3) a composite steady-state service network. The proposed framework allows process engineers to hierarchically compose model instances of service networks, which can represent production cells, lines, factory facilities and supply chains, and perform deterministic optimisation based on mathematical programming and Pareto-optimal trade-off analysis. Factory Optima is demonstrated using a case study of a service network for a heat sink product which involves contract vendors and manufacturing activities, including cutting, shearing, Computer Numerical Control (CNC) machining with milling and drilling operations, quality inspection, finishing, and assembly.

A benchmark dataset for the multiple depot vehicle scheduling problem.

This data article presents a description of a benchmark dataset for the multiple depot vehicle scheduling problem (MDVSP). The MDVSP is to assign vehicles from different depots to timetabled trips to minimize the total cost of empty travel and waiting. The dataset has been developed to evaluate the heuristics of the MDVSP that are presented in "A new formulation and a column generation-based heuristic for the multiple depot vehicle scheduling problem" (Kulkarni et al., 2018). The dataset contains 60 problem instances of varying size. Researchers can use the dataset to evaluate the future algorithms for the MDVSP and compare the performance with the existing algorithms. The dataset includes a program that can be used to generate new problem instances of the MDVSP.

Analysis and Optimization based on Reusable Knowledge Base of Process Performance Models.

In this paper, we propose an architectural design and software framework for fast development of descriptive, diagnostic, predictive, and prescriptive analytics solutions for dynamic production processes. The proposed architecture and framework will support the storage of modular, extensible, and reusable Knowledge Base (KB) of process performance models. The approach requires developing automated methods that can translate the high-level models in the reusable KB into low-level specialized models required by a variety of underlying analysis tools, including data manipulation, optimization, statistical learning, estimation, and simulation. We also propose an organization and key structure for the reusable KB, composed of atomic and composite process performance models and domain-specific dashboards. Furthermore, we illustrate the use of the proposed architecture and framework by prototyping a decision-support system for process engineers. The decision support system allows users to hierarchically compose and optimize dynamic production processes via a graphical user interface.

Identification of broadly neutralizing antibody epitopes in the HIV-1 envelope glycoprotein using evolutionary models.

BACKGROUND: Identification of the epitopes targeted by antibodies that can neutralize diverse HIV-1 strains can provide important clues for the design of a preventative vaccine. METHODS: We have developed a computational approach that can identify key amino acids within the HIV-1 envelope glycoprotein that influence sensitivity to broadly cross-neutralizing antibodies. Given a sequence alignment and neutralization titers for a panel of viruses, the method works by fitting a phylogenetic model that allows the amino acid frequencies at each site to depend on neutralization sensitivities. Sites at which viral evolution influences neutralization sensitivity were identified using Bayes factors (BFs) to compare the fit of this model to that of a null model in which sequences evolved independently of antibody sensitivity. Conformational epitopes were identified with a Metropolis algorithm that searched for a cluster of sites with large Bayes factors on the tertiary structure of the viral envelope. RESULTS: We applied our method to ID50 neutralization data generated from seven HIV-1 subtype C serum samples with neutralization breadth that had been tested against a multi-clade panel of 225 pseudoviruses for which envelope sequences were also available. For each sample, between two and four sites were identified that were strongly associated with neutralization sensitivity (2ln(BF) > 6), a subset of which were experimentally confirmed using site-directed mutagenesis. CONCLUSIONS: Our results provide strong support for the use of evolutionary models applied to cross-sectional viral neutralization data to identify the epitopes of serum antibodies that confer neutralization breadth.

A multiple-alignment based primer design algorithm for genetically highly variable DNA targets.

BACKGROUND: Primer design for highly variable DNA sequences is difficult, and experimental success requires attention to many interacting constraints. The advent of next-generation sequencing methods allows the investigation of rare variants otherwise hidden deep in large populations, but requires attention to population diversity and primer localization in relatively conserved regions, in addition to recognized constraints typically considered in primer design. RESULTS: Design constraints include degenerate sites to maximize population coverage, matching of melting temperatures, optimizing de novo sequence length, finding optimal bio-barcodes to allow efficient downstream analyses, and minimizing risk of dimerization. To facilitate primer design addressing these and other constraints, we created a novel computer program (PrimerDesign) that automates this complex procedure. We show its powers and limitations and give examples of successful designs for the analysis of HIV-1 populations. CONCLUSIONS: PrimerDesign is useful for researchers who want to design DNA primers and probes for analyzing highly variable DNA populations. It can be used to design primers for PCR, RT-PCR, Sanger sequencing, next-generation sequencing, and other experimental protocols targeting highly variable DNA samples.

Recurrent signature patterns in HIV-1 B clade envelope glycoproteins associated with either early or chronic infections.

Here we have identified HIV-1 B clade Envelope (Env) amino acid signatures from early in infection that may be favored at transmission, as well as patterns of recurrent mutation in chronic infection that may reflect common pathways of immune evasion. To accomplish this, we compared thousands of sequences derived by single genome amplification from several hundred individuals that were sampled either early in infection or were chronically infected. Samples were divided at the outset into hypothesis-forming and validation sets, and we used phylogenetically corrected statistical strategies to identify signatures, systematically scanning all of Env. Signatures included single amino acids, glycosylation motifs, and multi-site patterns based on functional or structural groupings of amino acids. We identified signatures near the CCR5 co-receptor-binding region, near the CD4 binding site, and in the signal peptide and cytoplasmic domain, which may influence Env expression and processing. Two signatures patterns associated with transmission were particularly interesting. The first was the most statistically robust signature, located in position 12 in the signal peptide. The second was the loss of an N-linked glycosylation site at positions 413-415; the presence of this site has been recently found to be associated with escape from potent and broad neutralizing antibodies, consistent with enabling a common pathway for immune escape during chronic infection. Its recurrent loss in early infection suggests it may impact fitness at the time of transmission or during early viral expansion. The signature patterns we identified implicate Env expression levels in selection at viral transmission or in early expansion, and suggest that immune evasion patterns that recur in many individuals during chronic infection when antibodies are present can be selected against when the infection is being established prior to the adaptive immune response.

Tree Pruner: An efficient tool for selecting data from a biased genetic database.

BACKGROUND: Large databases of genetic data are often biased in their representation. Thus, selection of genetic data with desired properties, such as evolutionary representation or shared genotypes, is problematic. Selection on the basis of epidemiological variables may not achieve the desired properties. Available automated approaches to the selection of influenza genetic data make a tradeoff between speed and simplicity on the one hand and control over quality and contents of the dataset on the other hand. A poorly chosen dataset may be detrimental to subsequent analyses. RESULTS: We developed a tool, Tree Pruner, for obtaining a dataset with desired evolutionary properties from a large, biased genetic database. Tree Pruner provides the user with an interactive phylogenetic tree as a means of editing the initial dataset from which the tree was inferred. The tree visualization changes dynamically, using colors and shading, reflecting Tree Pruner actions. At the end of a Tree Pruner session, the editing actions are implemented in the dataset. Currently, Tree Pruner is implemented on the Influenza Research Database (IRD). The data management capabilities of the IRD allow the user to store a pruned dataset for additional pruning or for subsequent analysis. Tree Pruner can be easily adapted for use with other organisms. CONCLUSIONS: Tree Pruner is an efficient, manual tool for selecting a high-quality dataset with desired evolutionary properties from a biased database of genetic sequences. It offers an important alternative to automated approaches to the same goal, by providing the user with a dynamic, visual guide to the ongoing selection process and ultimate control over the contents (and therefore quality) of the dataset.