LuSIV Cells: A Reporter Cell Line for the Detection and Quantitation of a Single Cycle of HIV and SIV Replication

AbstractA single cycle of viral replication is the time required for a virus to enter the host cell, replicate its genome, and produce infectious progeny virions. The primate lentiviruses, human immunodeficiency virus (HIV) and simian immunodeficiency virus (SIV), require on average 24 h to complete one cycle of replication. We have now developed and characterized a reporter assay system in CEMx174 cells for the quantitative measurement of HIV/SIV infection within a single replication cycle. The SIVmac239 LTR (−225 → +149) was cloned upstream of the firefly luciferase reporter gene and this reporter plasmid is maintained in CEMx174 cells under stable selection. This cell line, designated LuSIV, is highly sensitive to infection by primary and laboratory strains of HIV/SIV, resulting in Tat-mediated expression of luciferase, which correlates with viral infectivity. Furthermore, manipulation of LuSIV cells for the detection of luciferase activity is easy to perform and requires a minimal amount of time as compared to current HIV/SIV detection systems. The LuSIV system is a powerful tool for the analysis of HIV/SIV infection that provides a unique assay system that can detect virus replication prior to 24 h and does not require virus to spread from cell to cell. Thus these cells can be used for the study of replication-deficient viruses and the high throughput screening of antivirals, or other inhibitors of infection

Inferring yeast cell cycle regulators and interactions using transcription factor activities

BACKGROUND: Since transcription factors are often regulated at the post-transcriptional level, their activities, rather than expression levels may provide valuable information for investigating functions and their interactions. The recently developed Network Component Analysis (NCA) and its generalized form (gNCA) provide a robust framework for deducing the transcription factor activities (TFAs) from various types of DNA microarray data and transcription factor-gene connectivity. The goal of this work is to demonstrate the utility of TFAs in inferring transcription factor functions and interactions in Saccharomyces cerevisiae cell cycle regulation. RESULTS: Using gNCA, we determined 74 TFAs from both wild type and fkh1 fkh2 deletion mutant microarray data encompassing 1529 ORFs. We hypothesized that transcription factors participating in the cell cycle regulation exhibit cyclic activity profiles. This hypothesis was supported by the TFA profiles of known cell cycle factors and was used as a basis to uncover other potential cell cycle factors. By combining the results from both cluster analysis and periodicity analysis, we recovered nearly 90% of the known cell cycle regulators, and identified 5 putative cell cycle-related transcription factors (Dal81, Hap2, Hir2, Mss11, and Rlm1). In addition, by analyzing expression data from transcription factor knockout strains, we determined 3 verified (Ace2, Ndd1, and Swi5) and 4 putative interaction partners (Cha4, Hap2, Fhl1, and Rts2) of the forkhead transcription factors. Sensitivity of TFAs to connectivity errors was determined to provide confidence level of these predictions. CONCLUSION: By subjecting TFA profiles to analyses based upon physiological signatures we were able to identify cell cycle related transcription factors consistent with current literature, transcription factors with potential cell cycle dependent roles, and interactions between transcription factors

Intelligent Personal Assistants and the Intercultural Negotiations of Dataveillance in Platformed Households

The platformization of households is increasingly possible with the introduction of intelligent personal assistants (IPAs) embedded in smart, always-listening speakers and screens, such as Google Home and the Amazon Echo. These devices exemplify Zuboff\u27s surveillance capitalism by commodifying familial and social spaces and funneling data into corporate networks. However, the motivations driving the development of these platforms-and the dataveillance they afford-vary: Amazon appears focused on collecting user data to drive personalized sales across its shopping platform, while Google relies on its vast dataveillance infrastructure to build its Al-driven targeted advertising platform. This paper draws on cross-cultural focus groups regarding IPAs in the Netherlands and the United States. It reveals how respondents in these two countries articulate divergent ways of negotiating the dataveiLlance affordances and privacy concerns of these IPA platforms. These findings suggest the need for a nuanced approach to combating and limiting the potential harms of these home devices, which may otherwise be seen as equivalents

Automated Performance Characterization of DSN System Frequency Stability Using Spacecraft Tracking Data

This software provides an automated capability to measure and qualify the frequency stability performance of the Deep Space Network (DSN) ground system, using daily spacecraft tracking data. The results help to verify if the DSN performance is meeting its specification, therefore ensuring commitments to flight missions; in particular, the radio science investigations. The rich set of data also helps the DSN Operations and Maintenance team to identify the trends and patterns, allowing them to identify the antennas of lower performance and implement corrective action in a timely manner. Unlike the traditional approach where the performance can only be obtained from special calibration sessions that are both time-consuming and require manual setup, the new method taps into the daily spacecraft tracking data. This new approach significantly increases the amount of data available for analysis, roughly by two orders of magnitude, making it possible to conduct trend analysis with good confidence. The software is built with automation in mind for end-to-end processing. From the inputs gathering to computation analysis and later data visualization of the results, all steps are done automatically, making the data production at near zero cost. This allows the limited engineering resource to focus on high-level assessment and to follow up with the exceptions/deviations. To make it possible to process the continual stream of daily incoming data without much effort, and to understand the results quickly, the processing needs to be automated and the data summarized at a high level. Special attention needs to be given to data gathering, input validation, handling anomalous conditions, computation, and presenting the results in a visual form that makes it easy to spot items of exception/ deviation so that further analysis can be directed and corrective actions followed