Methods for Reconstructing Networks with Incomplete Information.

Network representations of complex systems are widespread and reconstructing unknown networks from data has been intensively researched in statistical and scientific communities more broadly. Two challenges in network reconstruction problems include having insufficient data to illuminate the full structure of the network and needing to combine information from different data sources. Addressing these challenges, this thesis contributes methodology for network reconstruction in three respects. First, we consider sequentially choosing interventions to discover structure in directed networks focusing on learning a partial order over the nodes. This focus leads to a new model for intervention data under which nodal variables depend on the lengths of paths separating them from intervention targets rather than on parent sets. Taking a Bayesian approach, we present partial-order based priors and develop a novel Markov-Chain Monte Carlo (MCMC) method for computing posterior expectations over directed acyclic graphs. The utility of the MCMC approach comes from designing new proposals for the Metropolis algorithm that move locally among partial orders while independently sampling graphs from each partial order. The resulting Markov Chains mix rapidly and are ergodic. We also adapt an existing strategy for active structure learning, develop an efficient Monte Carlo procedure for estimating the resulting decision function, and evaluate the proposed methods numerically using simulations and benchmark datasets. We next study penalized likelihood methods using incomplete order information as arising from intervention data. To make the notion of incomplete information precise, we introduce and formally define incomplete partial orders which subsumes the important special case of a known total ordering of the nodes. This special case lies along an information lattice and we study the reconstruction performance of penalized likelihood methods at different points along this lattice. Finally, we present a method for ranking a network's potential edges using time-course data. The novelty is our development of a nonparametric gradient-matching procedure and a related summary statistic for measuring the strength of relationships among components in dynamic systems. Simulation studies demonstrate that given sufficient signal moving using this procedure to move from linear to additive approximations leads to improved rankings of potential edges.PhDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113316/1/jbhender_1.pd

Command and control architecture for reconnaissance and counterreconnaissance in the U.S. Army armor and mechanized infantry task force

The author presents an analysis of reconnaissance and counterreconnaissance missions in the U.S. Army armor and mechanized infantry task force. An introduction to reconnaissance and counterreconnaissance provides background information essential to the analysis of each mission. The impact of information processing within the task force and its effect on mission execution is discussed. A systematic approach to mission, or task, analysis using four task variables (task characteristics, task environment, inter-unit task interdependence, technology) identifies the uncertainty in the task and the subsequent impact on information processing. An analysis of reconnaissance and counterreconnaissance using the four variables reveals the uncertainty in each task and its effect on the information processing capability of the task force. A unique command and control architecture is developed for each task which addresses the uncertainty in the task and facilitates information processing within the task forcehttp://archive.org/details/commandcontrolar00hendNANAU.S. Army (USA) autho

Production of membrane proteins for characterisation of their pheromone-sensing and antimicrobial resistance functions

AbstractDespite the importance of membrane proteins in cellular processes, studies of these hydrophobic proteins present major technical challenges, including expression and purification for structural and biophysical studies. A modified strategy of that proposed previously by Saidijam et al. (2005) and others, for the routine expression of bacterial membrane proteins involved in environmental sensing and antimicrobial resistance (AMR), is proposed which results in purification of sufficient proteins for biophysical experiments. We report expression successes amongst a collection of enterococcal vancomycin resistance membrane proteins: VanTG, VanTG-M transporter domain, VanZ and the previously characterised VanS (A-type) histidine protein kinase (HPK). Using the same strategy, we report on the successful amplification and purification of intact BlpH and ComD2 HPKs of Streptococcus pneumoniae. Near-UV circular dichroism revealed both recombinant proteins bound their pheromone ligands BlpC and CSP2. Interestingly, CSP1 also interacted with ComD. Finally, we evaluate the alternative strategy for studying sensory HPKs involving isolated soluble sensory domain fragments, exemplified by successful production of VicKESD of Enterococcus faecalis VicK. Purified VicKESD possessed secondary structure post-purification. Thermal denaturation experiments using far-UV CD, a technique which can be revealing regarding ligand binding, revealed that: (a) VicKESD denaturation occurs between 15 and 50 °C; and (b) reducing conditions did not detectably affect denaturation profiles suggesting reducing conditions per se are not directly sensed by VicKESD. Our findings provide information on a modified strategy for the successful expression, production and/or storage of bacterial membrane HPKs, AMR proteins and sensory domains for their future crystallisation, and ligand binding studies

Carbon Dioxide Sources from Alaska Driven by Increasing Early Winter Respiration from Artic Tundra

High-latitude ecosystems have the capacity to release large amounts of carbon dioxide (CO2) to the atmosphere in response to increasing temperatures, representing a potentially significant positive feedback within the climate system. Here, we combine aircraft and tower observations of atmospheric CO2 with remote sensing data and meteorological products to derive temporally and spatially resolved year-round CO2 fluxes across Alaska during 2012-2014. We find that tundra ecosystems were a net source of CO2 to the atmosphere annually, with especially high rates of respiration during early winter (October through December). Long-term records at Barrow, AK, suggest that CO2emission rates from North Slope tundra have increased during the October through December period by 73% ± 11% since 1975, and are correlated with rising summer temperatures. Together, these results imply increasing early winter respiration and net annual emission of CO2in Alaska, in response to climate warming. Our results provide evidence that the decadal-scale increase in the amplitude of the CO2 seasonal cycle may be linked with increasing biogenic emissions in the Arctic, following the growing season. Early winter respiration was not well simulated by the Earth System Models used to forecast future carbon fluxes in recent climate assessments. Therefore, these assessments may underestimate the carbon release from Arctic soils in response to a warming climate

Rapid elimination of CO through the lungs: coming full circle 100 years on

At the start of the 20th century, CO poisoning was treated by administering a combination of CO2 and O2 (carbogen) to stimulate ventilation. This treatment was reported to be highly effective, even reversing the deep coma of severe CO poisoning before patients arrived at the hospital. The efficacy of carbogen in treating CO poisoning was initially attributed to the absorption of CO2; however, it was eventually realized that the increase in pulmonary ventilation was the predominant factor accelerating clearance of CO from the blood. The inhaled CO2 in the carbogen stimulated ventilation but prevented hypocapnia and the resulting reductions in cerebral blood flow. By then, however, carbogen treatment for CO poisoning had been abandoned in favour of hyperbaric O2. Now, a half-century later, there is accumulating evidence that hyperbaric O2 is not efficacious, most probably because of delays in initiating treatment. We now also know that increases in pulmonary ventilation with O2-enriched gas can clear CO from the blood as fast, or very nearly as fast, as hyperbaric O2. Compared with hyperbaric O2, the technology for accelerating pulmonary clearance of CO with hyperoxic gas is not only portable and inexpensive, but also may be far more effective because treatment can be initiated sooner. In addition, the technology can be distributed more widely, especially in developing countries where the prevalence of CO poisoning is highest. Finally, early pulmonary CO clearance does not delay or preclude any other treatment, including subsequent treatment with hyperbaric O2

The Atacama Cosmology Telescope: Two-Season ACTPol Spectra and Parameters

We present the temperature and polarization angular power spectra measured by the Atacama Cosmology Telescope Polarimeter (ACTPol). We analyze night-time data collected during 2013-14 using two detector arrays at 149 GHz, from 548 deg$^2$ of sky on the celestial equator. We use these spectra, and the spectra measured with the MBAC camera on ACT from 2008-10, in combination with Planck and WMAP data to estimate cosmological parameters from the temperature, polarization, and temperature-polarization cross-correlations. We find the new ACTPol data to be consistent with the LCDM model. The ACTPol temperature-polarization cross-spectrum now provides stronger constraints on multiple parameters than the ACTPol temperature spectrum, including the baryon density, the acoustic peak angular scale, and the derived Hubble constant. Adding the new data to planck temperature data tightens the limits on damping tail parameters, for example reducing the joint uncertainty on the number of neutrino species and the primordial helium fraction by 20%.Comment: 23 pages, 25 figure

Solving the border control problem: evidence of enhanced face matching in individuals with extraordinary face recognition skills.

Photographic identity documents (IDs) are commonly used despite clear evidence that unfamiliar face matching is a difficult and error-prone task. The current study set out to examine the performance of seven individuals with extraordinary face recognition memory, so called “super recognisers” (SRs), on two face matching tasks resembling border control identity checks. In Experiment 1, the SRs as a group outperformed control participants on the “Glasgow Face Matching Test”, and some case-by-case comparisons also reached significance. In Experiment 2, a perceptually difficult face matching task was used: the “Models Face Matching Test”. Once again, SRs outperformed controls both on group and mostly in case-by-case analyses. These findings suggest that SRs are considerably better at face matching than typical perceivers, and would make proficient personnel for border control agencies

Morphological Alternations at the Intonational Phrase Edge

This article develops an analysis of a pair of morphological alternations in K\u27ichee\u27 (Mayan) that are conditioned at the right edge of intonational phrase boundaries. I propose a syntax-prosody mapping algorithm that derives intonational phrase boundaries from the surface syntax, and then argue that each alternation can be understood in terms of output optimization. The important fact is that a prominence peak is always rightmost in the intonational phrase, and so the morphological alternations occur in order to ensure an optimal host for this prominence peak. Finally, I consider the wider implications of the analysis for the architecture of the syntax-phonology interface, especially as it concerns late-insertion theories of morphology

An Image Statistics–Based Model for Fixation Prediction

The problem of predicting where people look at, or equivalently salient region detection, has been related to the statistics of several types of low-level image features. Among these features, contrast and edge information seem to have the highest correlation with the fixation locations. The contrast distribution of natural images can be adequately characterized using a two-parameter Weibull distribution. This distribution catches the structure of local contrast and edge frequency in a highly meaningful way. We exploit these observations and investigate whether the parameters of the Weibull distribution constitute a simple model for predicting where people fixate when viewing natural images. Using a set of images with associated eye movements, we assess the joint distribution of the Weibull parameters at fixated and non-fixated regions. Then, we build a simple classifier based on the log-likelihood ratio between these two joint distributions. Our results show that as few as two values per image region are already enough to achieve a performance comparable with the state-of-the-art in bottom-up saliency prediction