Search

Elucidating the complete network of protein-protein interactions, or interactome, is a fundamental goal of the post-genomic era, yet existing interactome maps are far from complete. To increase the throughput and resolution of interactome mapping, methods for protein-protein interaction discovery by co-migration have been introduced. However, accurate identification of interacting protein pairs within the resulting large-scale proteomic datasets is challenging. Consequently, most computational pipelines for co-migration data analysis incorporate external genomic datasets to distinguish interacting from non-interacting protein pairs. The effect of this procedure on interactome mapping is poorly understood. Here, we conduct a rigorous analysis of genomic data integration for interactome recovery across a large number of co-migration datasets, spanning diverse experimental and computational methods. We find that genomic data integration leads to an increase in the functional coherence of the resulting interactome maps, but this comes at the expense of a decrease in power to discover novel interactions. Importantly, putative novel interactions predicted by genomic data integration are no more likely to later be experimentally discovered than those predicted from co-migration data alone. Our results reveal a widespread and unappreciated limitation in a methodology that has been widely used to map the interactome of humans and model organisms.

Spinal cord injury (SCI) is a devastating neurological condition for which there are currently no effective treatment options to restore function. A major obstacle to the development of new therapies is our fragmentary understanding of the coordinated pathophysiological processes triggered by damage to the human spinal cord. Here, we describe a systems biology approach to integrate decades of small-scale experiments with unbiased, genome-wide gene expression from the human spinal cord, revealing a gene regulatory network signature of the pathophysiological response to SCI. Our integrative analyses converge on an evolutionarily conserved gene subnetwork enriched for genes associated with the response to SCI by small-scale experiments, and whose expression is upregulated in a severity-dependent manner following injury and downregulated in functional recovery. We validate the severity-dependent upregulation of this subnetwork in rodents in primary transcriptomic and proteomic studies. Our analysis provides systems-level view of the coordinated molecular processes activated in response to SCI.

Ultrathin films of Na3Bi on insulating substrates are desired for opening a bulk bandgap and generating the quantum spin Hall effect from a topological Dirac semimetal, though continuous films in the few nanometer regime have been difficult to realize. Here, we utilize alternating layer molecular beam epitaxy to achieve uniform and continuous single-crystal films of Na3Bi(0001) on insulating Al2O3(0001) substrates and demonstrate electrical transport on films with 3.8 nm thickness (4 unit cells). The high material quality is confirmed through reflection high-energy electron diffraction, scanning tunneling microscopy, x-ray diffraction, and x-ray photoelectron spectroscopy.

Purpose —The purpose of this paper is to explain how the introduction of the Internet and digital tools renews and enriches John Dewey's experimentalist model for teaching and learning with particular attention to the place of and resources for higher order thinking. Design/Approach/Methods —The methods include a close exposition of Dewey's classical texts, and a thought experiment introducing ICT elements into Dewey's design diagrams for teaching and learning. Findings —Dewey's model has inherent difficulties, and that digital technologies helps resolve them. Originality/Value —With the Internet and new digital tools, teachers can design new virtual learning spaces and learning activities. Learners can use online information and communication tools to act more effectively using higher-order thinking skills.

Aspergillus fumigatus (A. fumigatus) is a wide-spread fungus that is a potent allergen in hypersensitive individuals but also an opportunistic pathogen in immunocompromised patients. It reproduces asexually by releasing airborne conidiospores (conidia). Upon inhalation, fungal conidia are capable of reaching the airway epithelial cells (AECs) in bronchial and alveolar tissues. Previous studies have predominantly used submerged monolayer cultures for studying this host-pathogen interaction; however, these cultures do not recapitulate the mucocililary differentiation phenotype of the in vivo epithelium in the respiratory tract. Thus, the aim of this study was to use well-differentiated primary human bronchial epithelial cells (HBECs) grown at the air-liquid interface (ALI) to determine their transcriptomic and proteomic responses following interaction with A. fumigatus conidia. We visualized conidial interaction with HBECs using confocal laser scanning microscopy (CLSM), and applied NanoString nCounter and shotgun proteomics to assess gene expression changes in the human cells upon interaction with A. fumigatus conidia. Western blot analysis was used to assess the expression of top three differentially expressed proteins, CALR, SET and NUCB2. CLSM showed that, unlike submerged monolayer cultures, well-differentiated ALI cultures of primary HBECs were estimated to internalize less than 1% of bound conidia. Nevertheless, transcriptomic and proteomic analyses revealed numerous differentially expressed host genes; these were enriched for pathways including apoptosis/autophagy, translation, unfolded protein response and cell cycle (up-regulated); complement and coagulation pathways, iron homeostasis, nonsense mediated decay and rRNA binding (down-regulated). CALR and SET were confirmed to be up-regulated in ALI cultures of primary HBECs upon exposure to A. fumigatus via western blot analysis. Therefore, using transcriptomics and proteomics approaches, ALI models recapitulating the bronchial epithelial barrier in the conductive zone of the respiratory tract can provide novel insights to the molecular response of bronchial epithelial cells upon exposure to A. fumigatus conidia.

The problem of efficiently touring a theme park so as to minimize the amount of time spent in queues is an instance of the Traveling Salesman Problem with Time-Dependent Service Times (TSP-TS). In this paper, we present a mixed-integer linear programming formulation of the TSP-TS and describe a branch-and-cut algorithm based on this model. In addition, we develop a lower bound for the TSP-TS and describe two metaheuristic approaches for obtaining good quality solutions: a genetic algorithm and a tabu search algorithm. Using test instances motivated by actual theme park data, we conduct a computational study to compare the effectiveness of our algorithms.