Examination of Antiviral Resistance in Venezuelan Equine Encephalitis Virus

Venezuelan equine encephalitis virus (VEEV) is a New World Alphavirus that causes Venezuelan equine encephalitis (VEE), which is characterized by a febrile illness that can progress to neurological disease and death. While no major outbreaks of VEE have occurred since 1995, VEEV is a virus of concern as, in addition to its spread through mosquitos, it can be aerosolized and used as a bioweapon. Unfortunately, there are currently no FDA-approved vaccines or antivirals against VEEV. Efforts have been made to discover small molecules with an inhibitory effect on VEEV, but the potential for emergence of antiviral resistance to these compounds will remain a concern because VEEV is an RNA virus with a high mutation rate and grows to high titers. To examine the evolutionary trajectory of antiviral resistance in VEEV, we developed a next-generation sequencing pipeline to examine single-nucleotide polymorphisms that emerged after repeated passaging of the virus with increasing concentrations of antiviral compounds. In addition, we examined the effect of the microenvironment on the evolution of antiviral resistance, both in cell culture and mouse models. We found that VEEV evolves resistance to the compound ML336 and its derivatives through mutations in the nsP2 and nsP4 genes, but the number, timing of emergence, and the extent of penetrance of these SNPs depend on the compound. These mutations emerged more slowly when infecting an astrocyte cell line. We also found that neurons in the mouse brain did not impose a selective pressure on VEEV during an infection. These results demonstrate how the population dynamics of RNA viruses can be tracked over time and the extent to which they are affected by selective pressures, as well as opening questions about how viruses can mutate and adapt at the molecular level

Continuous measurements of real-life bidirectional pedestrian flows on a wide walkway

Employing partially overlapping overhead \kinectTMS sensors and automatic pedestrian tracking algorithms we recorded the crowd traffic in a rectilinear section of the main walkway of Eindhoven train station on a 24/7 basis. Beside giving access to the train platforms (it passes underneath the railways), the walkway plays an important connection role in the city. Several crowding scenarios occur during the day, including high- and low-density dynamics in uni- and bi-directional regimes. In this paper we discuss our recording technique and we illustrate preliminary data analyses. Via fundamental diagrams-like representations we report pedestrian velocities and fluxes vs. pedestrian density. Considering the density range $0$ - $1.1\,$ped/m$^2$, we find that at densities lower than $0.8\,$ped/m$^2$ pedestrians in unidirectional flows walk faster than in bidirectional regimes. On the opposite, velocities and fluxes for even bidirectional flows are higher above $0.8\,$ped/m$^2$.Comment: 9 pages, 7 figure

Quantum Diffusion on Molecular Tubes: Universal Scaling of the 1D to 2D Transition

The transport properties of disordered systems are known to depend critically on dimensionality. We study the diffusion coefficient of a quantum particle confined to a lattice on the surface of a tube, where it scales between the 1D and 2D limits. It is found that the scaling relation is universal and independent of the disorder and noise parameters, and the essential order parameter is the ratio between the localization length in 2D and the circumference of the tube. Phenomenological and quantitative expressions for transport properties as functions of disorder and noise are obtained and applied to real systems: In the natural chlorosomes found in light-harvesting bacteria the exciton transfer dynamics is predicted to be in the 2D limit, whereas a family of synthetic molecular aggregates is found to be in the homogeneous limit and is independent of dimensionality.Comment: 10 pages, 6 figure

How Satellites are Moving Beyond the Class System: Class Agnostic Development and Operations Approaches for Constraints-Driven Missions

Should we abolish the Class System? The Class A/B/C/D mission assurance and risk posture designations familiar to most satellite developers were established in 1986. They are used by both the Department of Defense (DoD) and National Aeronautics and Space Administration (NASA) to define risk and risk mitigation requirements for flight missions. However, many of today’s satellites are different – smaller, digitally engineered, designed for production, and increasingly destined for proliferated architectures. The rate of development is increasing while the uniqueness of the systems being built is decreasing. The need to move faster and the ability to utilize, for the first time in space, real product-line components challenges the premise and assumptions behind the Class A through D designations. The traditional “Class System” is not as applicable to most small satellite developments, which instead focus on ways to prioritize key, high impact, agile processes in an effort to cut costs and timelines. Operating within this environment requires satellite developers to apply practices that are agnostic to class definition (e.g., the practices that are most fundamental to ensuring the mission meets the needs). This paper outlines the Class Agnostic approach and constraints-based mission implementation practices. It will describe several real-life examples from Air Force Research Laboratory, Space and Missile System Center, and Space Rapid Capabilities Office missions that are applying a “class agnostic” approach to their missions. It will include lessons learned from missions which failed critical Do No Harm requirements and lost a flight to missions that have fully utilized the class agnostic approach. It will also discuss how the several missions used class-agnostic techniques to balance requirements of scope, risk, cost, and schedule to maximize the chances of mission success within hard constraints. The approaches used in these missions are applicable not only to small satellites, but also to any mission intending to move beyond the “Class System” to a more agile and flexible mindset for risk mitigation and mission assurance

Optimal Sub-Gaussian Mean Estimation in Very High Dimensions

We address the problem of mean estimation in very high dimensions, in the high probability regime parameterized by failure probability ?. For a distribution with covariance ?, let its "effective dimension" be d_eff = {Tr(?)}/{?_{max}(?)}. For the regime where d_eff = ?(log^2 (1/?)), we show the first algorithm whose sample complexity is optimal to within 1+o(1) factor. The algorithm has a surprisingly simple structure: 1) re-center the samples using a known sub-Gaussian estimator, 2) carefully choose an easy-to-compute positive integer t and then remove the t samples farthest from the origin and 3) return the sample mean of the remaining samples. The core of the analysis relies on a novel vector Bernstein-type tail bound, showing that under general conditions, the sample mean of a bounded high-dimensional distribution is highly concentrated around a spherical shell

Two-Stage Predict+Optimize for Mixed Integer Linear Programs with Unknown Parameters in Constraints

Consider the setting of constrained optimization, with some parameters unknown at solving time and requiring prediction from relevant features. Predict+Optimize is a recent framework for end-to-end training supervised learning models for such predictions, incorporating information about the optimization problem in the training process in order to yield better predictions in terms of the quality of the predicted solution under the true parameters. Almost all prior works have focused on the special case where the unknowns appear only in the optimization objective and not the constraints. Hu et al.~proposed the first adaptation of Predict+Optimize to handle unknowns appearing in constraints, but the framework has somewhat ad-hoc elements, and they provided a training algorithm only for covering and packing linear programs. In this work, we give a new \emph{simpler} and \emph{more powerful} framework called \emph{Two-Stage Predict+Optimize}, which we believe should be the canonical framework for the Predict+Optimize setting. We also give a training algorithm usable for all mixed integer linear programs, vastly generalizing the applicability of the framework. Experimental results demonstrate the superior prediction performance of our training framework over all classical and state-of-the-art methods

From Smelter Fumes to Silk Road Winds: Exploring Legal Responses to Transboundary Air Pollution over South Korea

As China‘s industrialization has entered full swing, transboundary pollution has swept eastward across the Manchurian Plain and the Yellow Sea into neighboring Northeast Asian countries. The desertification of Mongolia and Northwestern China due to global warming has fueled seasonal yellow dust storms descending on Korea in increased frequency and intensity in recent years, acting as a vector for various kinds of air pollution. On top of sulfur dioxide and nitrous oxide that cause acid deposition which, in turn, destroys crops and forests, southeasterly winds carry fine particulate matter, aerosols, ozone, and heavy metals with more significant negative consequences on the health of humans and other species. Soaring demand for energy in China (supplied mainly by coal-fired power plants) is casting deep uncertainty on regional air quality for the future, given the historically unprecedented scale and pace of deployment of plants in such a densely populated region. It is widely assumed that coal will be China‘s principal source of energy for many decades to come, comprising as much as 70% of energy demand. According to the IEA World Energy Outlook for 2011, China will account for more than half of the global share of coal use in 2020 with conservative assumptions. However, as China rapidly becomes a major world market for internal combustion vehicles, increasing carbon monoxide emissions from vehicles are expected to contribute heavily to transboundary pollution in Asia and overtake power plants as the primary source of air pollution

The role of kidney injury molecule-1 in the metastasis of renal cell carcinoma

Over 30% of patients with renal cell carcinoma (RCC) present with metastases, with median survival of 2 years. Kidney injury molecule 1 (KIM-1) is a cell-surface glycoprotein expressed by \u3e90% of RCC tumours. Preliminary data mined from The Cancer Genome Atlas RNA-sequencing database indicates that KIM-1 overexpression predicts overall survival in patients. Here we sought to determine if tumour KIM-1 plays a role in RCC cell extravasation and metastasis to the lungs. In vitro invasion and in vivo metastasis assays were performed to investigate the metastatic potential of KIM-1-expressing cells, and RNA-seq was conducted to identify differentially expressed genes between KIM-1+ and KIM-1neg cells. Invasion was significantly decreased in vitro in both murine and human RCC cells that expressed KIM-1. We concluded that KIM-1 inhibits RCC extravasation and metastasis to the lungs, independent of adaptive immunity. RNA-seq analysis provided putative downstream effectors of KIM-1