Stable isotope profiles reveal active production of VOCs from human-associated microbes.

Volatile organic compounds (VOCs) measured from exhaled breath have great promise for the diagnosis of bacterial infections. However, determining human or microbial origin of VOCs detected in breath remains a great challenge. For example, the microbial fermentation product 2,3-butanedione was recently found in the breath of Cystic Fibrosis (CF) patients; parallel culture-independent metagenomic sequencing of the same samples revealed that Streptococcus and Rothia spp. have the genetic capacity to produce 2,3-butanedione. To investigate whether the genetic capacity found in metagenomes translates to bacterial production of a VOC of interest such as 2,3-butanedione, we fed stable isotopes to three bacterial strains isolated from patients: two gram-positive bacteria, Rothia mucilaginosa and Streptococcus salivarius, and a dominant opportunistic gram-negative pathogen, Pseudomonas aeruginosa. Culture headspaces were collected and analyzed using a gas chromatographic system to quantify the abundance of VOCs of interest; mass spectroscopy was used to determine whether the stable isotope label had been incorporated. Our results show that R. mucilaginosa and S. salivarius consumed D-Glucose-13C6 to produce labeled 2,3-butanedione. R. mucilaginosa and S. salivarius also produced labeled acetaldehyde and ethanol when grown with 2H2O. Additionally, we find that P. aeruginosa growth and dimethyl sulfide production are increased when exposed to lactic acid in culture. These results highlight the importance VOCs produced by P. aeruginosa, R. mucilaginosa, and S. salivarius as nutrients and signals in microbial communities, and as potential biomarkers in a CF infection

Searching for Z′Z' bosons decaying to gluons

The production and decay of a new heavy vector boson, a chromophilic $Z'$ vector boson, is described. The chromophilic $Z'$ couples only to two gluons, but its two-body decays are absent, leading to a dominant decay mode of $Z'\rightarrow q\bar{q}g$. The unusual nature of the interaction predicts a cross-section which grows with $m_{Z'}$ for a fixed coupling and an accompanying gluon with a coupling that rises with its energy. We study the $t\bar{t}g$ decay mode, proposing distinct reconstruction techniques for the observation of an excess and for the measurement of $m_{Z'}$. We estimate the sensitivity of current experimental datasets.Comment: For submission to PR

Information barrier functional requirements

for the purpose of this paper, the authors have used the term functional requirement to indicate a required task rather than the recommended method for accomplishing this task. The creation of effective information barrier technology will proceed as a series of steps: (1) IB conceptual Description; (2) IB Functional Requirements (this document--ongoing); (3) IB hardware and software specification; (4) IB hardware and software construction; and (5) IB implementation. This functional requirements document is not intended to supplant or supersede the conceptual description; rather, these functional requirements are intended to be used along with the earlier description to help generate hardware and software requirements

Simulation of Facility Operations and Materials Accounting for a Combined Reprocessing/Mox Fuel Fabrication Facility

We are developing a computer model of facility operations and nuclear materials accounting for a facility that reprocesses spent fuel and fabricates mixed oxide (MOX) fuel rods and assemblies from the recovered uranium and plutonium. The model will be used to determine the effectiveness of various materials measurement strategies for the facility and, ultimately, of other facility safeguards functions as well. This portion of the facility consists of a spent fuel storage pond, fuel shear, dissolver, clarifier, three solvent-extraction stages with uranium-plutonium separation after the first stage, and product concentrators. In this facility area mixed oxide is formed into pellets, the pellets are loaded into fuel rods, and the fuel rods are fabricated into fuel assemblies. These two facility sections are connected by a MOX conversion line in which the uranium and plutonium solutions from reprocessing are converted to mixed oxide. The model of the intermediate MOX conversion line used in the model is based on a design provided by Mike Ehinger of Oak Ridge National Laboratory (private communication). An initial version of the simulation model has been developed for the entire MOX conversion and fuel fabrication sections of the reprocessing/MOX fuel fabrication facility, and this model has been used to obtain inventory difference variance estimates for those sections of the facility. A significant fraction of the data files for the fuel reprocessing section have been developed, but these data files are not yet complete enough to permit simulation of reprocessing operations in the facility. Accordingly, the discussion in the following sections is restricted to the MOX conversion and fuel fabrication lines. 3 tabs

Neuroevolutionary reinforcement learning for generalized control of simulated helicopters

This article presents an extended case study in the application of neuroevolution to generalized simulated helicopter hovering, an important challenge problem for reinforcement learning. While neuroevolution is well suited to coping with the domain’s complex transition dynamics and high-dimensional state and action spaces, the need to explore efficiently and learn on-line poses unusual challenges. We propose and evaluate several methods for three increasingly challenging variations of the task, including the method that won first place in the 2008 Reinforcement Learning Competition. The results demonstrate that (1) neuroevolution can be effective for complex on-line reinforcement learning tasks such as generalized helicopter hovering, (2) neuroevolution excels at finding effective helicopter hovering policies but not at learning helicopter models, (3) due to the difficulty of learning reliable models, model-based approaches to helicopter hovering are feasible only when domain expertise is available to aid the design of a suitable model representation and (4) recent advances in efficient resampling can enable neuroevolution to tackle more aggressively generalized reinforcement learning tasks

Oral microbial communities in children, caregivers, and associations with salivary biomeasures and environmental tobacco smoke exposure

Human oral microbial communities are diverse, with implications for oral and systemic health. Oral microbial communities change over time; thus, it is important to understand how healthy versus dysbiotic oral microbiomes differ, especially within and between families. There is also a need to understand how the oral microbiome composition is changed within an individual including by factors such as environmental tobacco smoke (ETS) exposure, metabolic regulation, inflammation, and antioxidant potential. Using archived saliva samples collected from caregivers and children during a 90-month follow-up assessment in a longitudinal study of child development in the context of rural poverty, we used 16S rRNA gene sequencing to determine the salivary microbiome. A total of 724 saliva samples were available, 448 of which were from caregiver/child dyads, an additional 70 from children and 206 from adults. We compared children’s and caregivers’ oral microbiomes, performed “stomatotype” analyses, and examined microbial relations with concentrations of salivary markers associated with ETS exposure, metabolic regulation, inflammation, and antioxidant potential (i.e., salivary cotinine, adiponectin, C-reactive protein, and uric acid) assayed from the same biospecimens. Our results indicate that children and caregivers share much of their oral microbiome diversity, but there are distinct differences. Microbiomes from intrafamily individuals are more similar than microbiomes from nonfamily individuals, with child/caregiver dyad explaining 52% of overall microbial variation. Notably, children harbor fewer potential pathogens than caregivers, and participants’ microbiomes clustered into two groups, with major differences being driven by Streptococcus spp. Differences in salivary microbiome composition associated with ETS exposure, and taxa associated with salivary analytes representing potential associations between antioxidant potential, metabolic regulation, and the oral microbiome

Use of information barriers to protect classified information

This paper discusses the detailed requirements for an information barrier (IB) for use with verification systems that employ intrusive measurement technologies. The IB would protect classified information in a bilateral or multilateral inspection of classified fissile material. Such a barrier must strike a balance between providing the inspecting party the confidence necessary to accept the measurement while protecting the inspected party`s classified information. The authors discuss the structure required of an IB as well as the implications of the IB on detector system maintenance. A defense-in-depth approach is proposed which would provide assurance to the inspected party that all sensitive information is protected and to the inspecting party that the measurements are being performed as expected. The barrier could include elements of physical protection (such as locks, surveillance systems, and tamper indicators), hardening of key hardware components, assurance of capabilities and limitations of hardware and software systems, administrative controls, validation and verification of the systems, and error detection and resolution. Finally, an unclassified interface could be used to display and, possibly, record measurement results. The introduction of an IB into an analysis system may result in many otherwise innocuous components (detectors, analyzers, etc.) becoming classified and unavailable for routine maintenance by uncleared personnel. System maintenance and updating will be significantly simplified if the classification status of as many components as possible can be made reversible (i.e. the component can become unclassified following the removal of classified objects)

Detecting errors and anomalies in computerized materials control and accountability databases

The Automated MC and A Database Assessment project is aimed at improving anomaly and error detection in materials control and accountability (MC and A) databases and increasing confidence in the data that they contain. Anomalous data resulting in poor categorization of nuclear material inventories greatly reduces the value of the database information to users. Therefore it is essential that MC and A data be assessed periodically for anomalies or errors. Anomaly detection can identify errors in databases and thus provide assurance of the integrity of data. An expert system has been developed at Los Alamos National Laboratory that examines these large databases for anomalous or erroneous data. For several years, MC and A subject matter experts at Los Alamos have been using this automated system to examine the large amounts of accountability data that the Los Alamos Plutonium Facility generates. These data are collected and managed by the Material Accountability and Safeguards System, a near-real-time computerized nuclear material accountability and safeguards system. This year they have expanded the user base, customizing the anomaly detector for the varying requirements of different groups of users. This paper describes the progress in customizing the expert systems to the needs of the users of the data and reports on their results

APRIL: Active Preference-learning based Reinforcement Learning

This paper focuses on reinforcement learning (RL) with limited prior knowledge. In the domain of swarm robotics for instance, the expert can hardly design a reward function or demonstrate the target behavior, forbidding the use of both standard RL and inverse reinforcement learning. Although with a limited expertise, the human expert is still often able to emit preferences and rank the agent demonstrations. Earlier work has presented an iterative preference-based RL framework: expert preferences are exploited to learn an approximate policy return, thus enabling the agent to achieve direct policy search. Iteratively, the agent selects a new candidate policy and demonstrates it; the expert ranks the new demonstration comparatively to the previous best one; the expert's ranking feedback enables the agent to refine the approximate policy return, and the process is iterated. In this paper, preference-based reinforcement learning is combined with active ranking in order to decrease the number of ranking queries to the expert needed to yield a satisfactory policy. Experiments on the mountain car and the cancer treatment testbeds witness that a couple of dozen rankings enable to learn a competent policy