Selecting reusable components using algebraic specifications

A significant hurdle confronts the software reuser attempting to select candidate components from a software repository - discriminating between those components without resorting to inspection of the implementation(s). We outline a mixed classification/axiomatic approach to this problem based upon our lattice-based faceted classification technique and Guttag and Horning's algebraic specification techniques. This approach selects candidates by natural language-derived classification, by their interfaces, using signatures, and by their behavior, using axioms. We briefly outline our problem domain and related work. Lattice-based faceted classifications are described; the reader is referred to surveys of the extensive literature for algebraic specification techniques. Behavioral support for reuse queries is presented, followed by the conclusions

A hybrid approach to software repository retrieval: Blending faceted classification and type signatures

We present a user interface for software reuse repository that relies both on the informal semantics of faceted classification and the formal semantics of type signatures for abstract data types. The result is an interface providing both structural and qualitative feedback to a software reuser

Neural network-based retrieval from software reuse repositories

A significant hurdle confronts the software reuser attempting to select candidate components from a software repository - discriminating between those components without resorting to inspection of the implementation(s). We outline an approach to this problem based upon neural networks which avoids requiring the repository administrators to define a conceptual closeness graph for the classification vocabulary

Design of a lattice-based faceted classification system

We describe a software reuse architecture supporting component retrieval by facet classes. The facets are organized into a lattice of facet sets and facet n-tuples. The query mechanism supports precise retrieval and flexible browsing

Balancing generality and specificity in component-based reuse

For a component industry to be successful, we must move beyond the current techniques of black box reuse and genericity to a more flexible framework supporting customization of components as well as instantiation and composition of components. Customization of components strikes a balanced between creating dozens of variations of a base component and requiring the overhead of unnecessary features of an 'everything but the kitchen sink' component. We argue that design and instantiation of reusable components have competing criteria - design-for-use strives for generality, design-with-reuse strives for specificity - and that providing mechanisms for each can be complementary rather than antagonistic. In particular, we demonstrate how program slicing techniques can be applied to customization of reusable components

Inheritance for software reuse: The good, the bad, and the ugly

Inheritance is a powerful mechanism supported by object-oriented programming languages to facilitate modifications and extensions of reusable software components. This paper presents a taxonomy of the various purposes for which an inheritance mechanism can be used. While some uses of inheritance significantly enhance software reuse, some others are not as useful and in fact, may even be detrimental to reuse. The paper discusses several examples, and argues for a programming language design that is selective in its support for inheritance

A neural net-based approach to software metrics

Software metrics provide an effective method for characterizing software. Metrics have traditionally been composed through the definition of an equation. This approach is limited by the fact that all the interrelationships among all the parameters be fully understood. This paper explores an alternative, neural network approach to modeling metrics. Experiments performed on two widely accepted metrics, McCabe and Halstead, indicate that the approach is sound, thus serving as the groundwork for further exploration into the analysis and design of software metrics

Dynamic Analysis of Vascular Morphogenesis Using Transgenic Quail Embryos

Background: One of the least understood and most central questions confronting biologists is how initially simple clusters or sheet-like cell collectives can assemble into highly complex three-dimensional functional tissues and organs. Due to the limits of oxygen diffusion, blood vessels are an essential and ubiquitous presence in all amniote tissues and organs. Vasculogenesis, the de novo self-assembly of endothelial cell (EC) precursors into endothelial tubes, is the first step in blood vessel formation [1]. Static imaging and in vitro models are wholly inadequate to capture many aspects of vascular pattern formation in vivo, because vasculogenesis involves dynamic changes of the endothelial cells and of the forming blood vessels, in an embryo that is changing size and shape. Methodology/Principal Findings: We have generated Tie1 transgenic quail lines Tg(tie1:H2B-eYFP) that express H2B-eYFP in all of their endothelial cells which permit investigations into early embryonic vascular morphogenesis with unprecedented clarity and insight. By combining the power of molecular genetics with the elegance of dynamic imaging, we follow the precise patterning of endothelial cells in space and time. We show that during vasculogenesis within the vascular plexus, ECs move independently to form the rudiments of blood vessels, all while collectively moving with gastrulating tissues that flow toward the embryo midline. The aortae are a composite of somatic derived ECs forming its dorsal regions and the splanchnic derived ECs forming its ventral region. The ECs in the dorsal regions of the forming aortae exhibit variable mediolateral motions as they move rostrally; those in more ventral regions show significant lateral-to-medial movement as they course rostrally. Conclusions/Significance: The present results offer a powerful approach to the major challenge of studying the relative role(s) of the mechanical, molecular, and cellular mechanisms of vascular development. In past studies, the advantages of the molecular genetic tools available in mouse were counterbalanced by the limited experimental accessibility needed for imaging and perturbation studies. Avian embryos provide the needed accessibility, but few genetic resources. The creation of transgenic quail with labeled endothelia builds upon the important roles that avian embryos have played in previous studies of vascular development

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19

A Novel Neurotrophic Drug for Cognitive Enhancement and Alzheimer's Disease

Currently, the major drug discovery paradigm for neurodegenerative diseases is based upon high affinity ligands for single disease-specific targets. For Alzheimer's disease (AD), the focus is the amyloid beta peptide (Aß) that mediates familial Alzheimer's disease pathology. However, given that age is the greatest risk factor for AD, we explored an alternative drug discovery scheme that is based upon efficacy in multiple cell culture models of age-associated pathologies rather than exclusively amyloid metabolism. Using this approach, we identified an exceptionally potent, orally active, neurotrophic molecule that facilitates memory in normal rodents, and prevents the loss of synaptic proteins and cognitive decline in a transgenic AD mouse model