Function of the Signal Peptide and N- and C-terminal Propeptides in the Leucine Aminopeptidase from \u3cem\u3eAeromonas proteolytica\u3c/em\u3e

The leucine aminopeptidase from Aeromonas proteolytica (also known as Vibrio proteolyticus) (AAP) is a metalloenzyme with broad substrate specificity. The open reading frame (ORF) for AAP encodes a 54 kDa enzyme, however, the extracellular enzyme has a molecular weight of 43 kDa. This form of AAP is further processed to a mature, thermostable 32 kDa form but the exact nature of this process is unknown. Over-expression of different forms of AAP in Escherichia coli (with AAP\u27s native leader sequence, with and without the N- and/or C-terminal propeptides, and as fusion protein) has allowed a model for the processing of wild-type AAP to be proposed. The role of the A. proteolytica signal peptide in protein secretion as well as comparison to other known signal peptides reveals a close resemblance of the A. proteolytica signal peptide to the outer membrane protein (OmpA) signal peptide. Over-expression of the full 54 kDa AAP enzyme provides an enzyme that is significantly less active, due to a cooperative inhibitory interaction between both propeptides. Over-expression of AAP lacking its C-terminal propeptide provided an enzyme with an identical kcat value to wild-type AAP but exhibited a larger Km value, suggesting competitive inhibition of AAP by the N-terminal propeptide (Ki∼0.13 nM). The recombinant 32 kDa form of AAP was characterized by kinetic and spectroscopic methods and was shown to be identical to mature, wild-type AAP. Therefore, the ease of purification and processing of rAAP along with the fact that large quantities can be obtained now allow new detailed mechanistic studies to be performed on AAP through site-directed mutagenesis

Statistical aspects of omics data analysis using the random compound covariate

BACKGROUND: Dealing with high dimensional markers, such as gene expression data obtained using microarray chip technology or genomics studies, is a key challenge because the numbers of features greatly exceeds the number of biological samples. After selecting biologically relevant genes, how to summarize the expression of selected genes and then further build predicted model is an important issue in medical applications. One intuitive method of addressing this challenge assigns different weights to different features, subsequently combining this information into a single score, named the compound covariate. Investigators commonly employ this score to assess whether an association exists between the compound covariate and clinical outcomes adjusted for baseline covariates. However, we found that some clinical papers concerned with such analysis report bias p-values based on flawed compound covariate in their training data set. RESULTS: We correct this flaw in the analysis and we also propose treating the compound score as a random covariate, to achieve more appropriate results and significantly improve study power for survival outcomes. With this proposed method, we thoroughly assess the performance of two commonly used estimated gene weights through simulation studies. When the sample size is 100, and censoring rates are 50%, 30%, and 10%, power is increased by 10.6%, 3.5%, and 0.4%, respectively, by treating the compound score as a random covariate rather than a fixed covariate. Finally, we assess our proposed method using two publicly available microarray data sets. CONCLUSION: In this article, we correct this flaw in the analysis and the propose method, treating the compound score as a random covariate, can achieve more appropriate results and improve study power for survival outcomes

Approaches for Identifying Consumer Preferences for the Design of Technology Products: A Case Study of Residential Solar Panels

This paper investigates ways to obtain consumer preferences for technology products to help designers identify the key attributes that contribute to a product's market success. A case study of residential photovoltaic panels is performed in the context of the California, USA, market within the 2007–2011 time span. First, interviews are conducted with solar panel installers to gain a better understanding of the solar industry. Second, a revealed preference method is implemented using actual market data and technical specifications to extract preferences. The approach is explored with three machine learning methods: Artificial neural networks (ANN), Random Forest decision trees, and Gradient Boosted regression. Finally, a stated preference self-explicated survey is conducted, and the results using the two methods compared. Three common critical attributes are identified from a pool of 34 technical attributes: power warranty, panel efficiency, and time on market. From the survey, additional nontechnical attributes are identified: panel manufacturer's reputation, name recognition, and aesthetics. The work shows that a combination of revealed and stated preference methods may be valuable for identifying both technical and nontechnical attributes to guide design priorities.Center for Scalable and Integrated Nanomanufacturin

Electrical control of spins and giant g-factors in ring-like coupled quantum dots

Emerging theoretical concepts for quantum technologies have driven a continuous search for structures where a quantum state, such as spin, can be manipulated efficiently. Central to many concepts is the ability to control a system by electric and magnetic fields, relying on strong spin-orbit interaction and a large g-factor. Here, we present a new mechanism for spin and orbital manipulation using small electric and magnetic fields. By hybridizing specific quantum dot states at two points inside InAs nanowires, nearly perfect quantum rings form. Large and highly anisotropic effective g-factors are observed, explained by a strong orbital contribution. Importantly, we find that the orbital and spin-orbital contributions can be efficiently quenched by simply detuning the individual quantum dot levels with an electric field. In this way, we demonstrate not only control of the effective g-factor from 80 to almost 0 for the same charge state, but also electrostatic change of the ground state spin

Why Is Infant Mortality Higher in the United States than in Europe?

The United States has higher infant mortality than peer countries. In this paper, we combine microdata from the United States with similar data from four European countries to investigate this US infant mortality disadvantage. The US disadvantage persists after adjusting for potential differential reporting of births near the threshold of viability. While the importance of birth weight varies across comparison countries, relative to all comparison countries the United States has similar neonatal (<1 month) mortality but higher postneonatal (1–12 months) mortality. We document similar patterns across census divisions within the United States. The postneonatal mortality disadvantage is driven by poor birth outcomes among lower socioeconomic status individuals. (JEL I12, I14, I32, J14)National Institute on Aging (Grant Number T32-AG000186)National Science Foundation (U.S.) (Grant Number 1151497

Propagating Uncertainty in Solar Panel Performance for Life Cycle Modeling in Early Stage Design

One of the challenges in accurately applying metrics for life cycle assessment lies in accounting for both irreducible and inherent uncertainties in how a design will perform under real world conditions. This paper presents a preliminary study that compares two strategies, one simulation-based and one set-based, for propagating uncertainty in a system. These strategies for uncertainty propagation are then aggregated. This work is conducted in the context of an amorphous photovoltaic (PV) panel, using data gathered from the National Solar Radiation Database, as well as realistic data collected from an experimental hardware setup specifically for this study. Results show that the influence of various sources of uncertainty can vary widely, and in particular that solar radiation intensity is a more significant source of uncertainty than the efficiency of a PV panel. This work also shows both set-based and simulation-based approaches have limitations and must be applied thoughtfully to prevent unrealistic results. Finally, it was found that aggregation of the two uncertainty propagation methods provided faster results than either method alone.Center for Scalable and Integrated NanomanufacturingNational Science Foundation (U.S.) (Nanoscale Science and Engineering Center

Implementation of an integrated continuous downstream process for a monoclonal antibody production

The biopharmaceutical market is driving the revolution from batch to continuous manufacturing (CM) for higher productivity and lower cost. In this work, a bench-scale fully integrated continuous downstream process for monoclonal antibody production was established and successfully scaled up to 200 L scale. The process includes a continuous proteinA step, a viral inactivation step, a batch-wise cation exchange and anion exchange step, a batch-wise viral-filtration step, and a single-pass UF/DF step. An inline protein quantity monitoring system was designed to control protein loading mass on cation exchange column. All the steps were connected through surge tanks and integrated by DeltaVTM automatic control system. Please download the PDF file for full content