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Four microbacteriophages infecting the host Microbacterium foliorum were isolated at Gonzaga University as part of the SEA-PHAGES program. Phages Teehee, StrawberryJamm, Quammi, and Casend are in the EG cluster, with average genome sizes of 62,263 bp and GC contents of 67.2%, with other interesting characteristics.

BACKGROUND AND OBJECTIVES Children living with medical complexity (CMC) experience frequent hospital admissions. Validated patient-reported experience measures may inform care improvements in this cohort. Our objectives were to examine the comprehensive inpatient experience of CMC by using a validated patient-reported experience measure and compare the results with all other respondents at 2 academic pediatric hospitals in a western Canadian province. METHODS Parents completed the Child Hospital Consumer Assessment of Healthcare Providers and Systems survey. Surveys were linked with inpatient records, and an accepted case definition was used to extract records pertaining to CMC. Results were reported as percent in “top box,” represented by the most positive answer choice to each measure. Odds of reporting a top box response were calculated while controlling for demographic and clinical features. RESULTS From October 2015 to March 2019, 4197 surveys (1515 CMC; 2682 non-CMC) were collected. Among CMC, the highest-rated measures pertained to being kept informed while in the emergency department, a willingness to recommend the hospital, and parents having a clear understanding of their role in their child’s care. The lowest-rated measures pertained to preventing mistakes and reporting concerns and the quietness of the hospital room at night. Compared with others, parents of CMC reported lower raw results on 20 of the 28 measures. They also reported lower a odds of reporting a top box score on 2 measures and higher odds on 1. CONCLUSIONS Parents of CMC revealed many perceived gaps. These findings can be used to inform strategies to improve care among CMC and policies to support the care of CMC and their families.

The role of so-called pest species in alternatively causing, or responding to, changing rangeland conditions remains contentious. Most scientists have concluded that high densities of plateau pikas (Ochotona curzoniae) on the Qinghai-Tibet Plateau, China, are a response to sparsely vegetation conditions, but demographic studies to support this hypothesis are lacking. During February–July 2009 we captured, weighed, and released plateau pikas monthly on 16 plots arrayed along a continuum of vegetation cover and pika density, both of which reflected differing land use for livestock management. We estimated apparent survival of adults using the Cormack–Jolly–Seber approach and recruitment of juveniles using the Pradel approach (in program MARK), modeling both as functions of habitat conditions, including vegetation cover. The probability of an adult pika captured in February being alive and present where initially captured in late July was ~0.04; male and female survival rates were similar. Best-supported models of survival included vegetation cover and an index of late winter pika density; survival was negatively related to percent vegetation cover. Pika body condition varied monthly, and was negatively associated with percent vegetation cover during February–May, but positively associated in June. Pikas living where yaks had reduced vegetation cover displayed no lower recruitment, and higher survival, than where vegetation cover was higher. Thus, our results support the hypothesis that high pika density is a response to, rather than a cause of, ‘‘rangeland degradation.’’ Reducing undesired high pika densities will require addressing root causes of undesirable rangeland conditions.

No writer is more closely bound up with our deepest sense of the meaning of the “American” than Thomas Jefferson and it is difficult to imagine America's national purpose without some reference to his words. Yet Jefferson's projection of American identity also assumed and even constituted, of necessity, the un-American and it is in this sense that the un-American provided the necessary contours of what became the “American.” Jefferson's various projects are often seen in tension with one another. But this dialectic between the American and the un-American helps reconcile many of them. Federalists, Jefferson believed, assumed that governing Americans demanded the force and corruption that had long kept Europeans in order, whereas Americans, he believed, had an experience of history that rendered them capable of transcending such political theory and practicing democratic politics. This paper explores this dialectic between the American and the un-American in Jefferson's thought as a problem of national self-definition and argues that Jefferson's overwhelming confidence about American identity rested to a large degree in the shudder produced by his experience of the other. Years before Joseph McCarthy and HUAC, Jefferson's project of defining the nation created the un-American, rendering Americans ever since profoundly, however paradoxically, ambivalent about the prospects for revolutionary republicanism abroad.

In this chapter we consider situations in which a single host computer is inadequate because the data volume or processing demand exceeds the capacity of the host. A popular solution distributes the data and computations across a network of computers or a short-lived network created for the task (a cluster). In this scenario, each cluster node (a computing unit) stores and processes a subset of the data. The results are merged as one when all nodes have been completed their tasks. For this solution to succeed, the computational algorithm must conform to a certain structure and the cluster execution must be managed. The Hadoop environment and the MapReduce programming design provide the management and algorithmic structure. Hadoop is a collection of software and services that builds the cluster, distributes the data across the cluster, and controls the data processing algorithms and the transmission of results. The MapReduce programming design insures scalability, and scalability insures that the results are independent of the cluster configuration. The reader is guided through an introductory application of Hadoop and MapReduce after a discussion of the essential components.

Streaming data are data transmitted by a source to a host computer immediately after being produced. The intent of real-time data analytics is to analyze the data as they are received and at a rate sufficiently fast to keep up with the stream. Analyses of streaming data center about characterizing the current level of the data-generating process, forecasting future values, and determining whether the process is undergoing unexpected change. In this chapter, we focus on computational aspects of real-time analytics. Methods were discussed in Chap. 11 The tutorials guide the reader through the analysis of data streams originating from two public and very different sources: tweets originating from the Twitter API and stock quotations originating from the NASDAQ.

The purpose of the k-nearest neighbor prediction function is to predict a target variable from a predictor vector. Commonly, the target is a categorical variable, a label identifying the group from which the observation was drawn. The analyst has no knowledge of the membership label but does have the information coded in the attributes of the predictor vector. The predictor vector and the k-nearest neighbor prediction function generate a prediction of membership. In addition to qualitative attributes, the k-nearest neighbor prediction function may be used to predict quantitative target variables. The k-nearest-neighbor prediction functions are conceptually and computationally simple and often rival far more sophisticated prediction functions with respect to accuracy. The functions are nonparametric in the sense that the mathematical basis supporting the prediction functions is not a model. Instead the k-nearest neighbor prediction function utilizes a set of training observations on target and predictor vector pairs and, in essence examines the target values of the training observations nearest to the target. If the target variable is a group membership label, the target is predicted to be to the most common label among the nearest neighbors. If the target is quantitative, then the prediction is an average of the target values associated with the nearest neighbors.

It’s not uncommon that a single computer is inadequate to handle a massively large data set. The common problems are that it takes too long to process the data and the data volume exceeds the storage capacity of the host. Cleverly designed algorithms sometimes can reduce the processing time to an acceptable point, but the single host solution will eventually fail if data volume is sufficiently great. A far-reaching solution to the data volume problem replaces the single host with a network of computers across which the data are distributed and processed. However, the hardware solution is incomplete until the data processing algorithms are adapted to the distributed computing environment. A complete solution requires algorithms that are scalable. Scalability depends on the statistics that are being computed by the algorithm, and the statistics that allow for scalability are associative statistics. Scalability and associative statistics are the subject of this chapter.

The naive Bayes prediction function is a computationally and conceptually simple algorithm. While the performance of the algorithm generally is not best among competitors when the predictor variables are quantitative, it does well with categorical predictor variables, and it’s especially well-suited for categorical predictor variables with many categories. In this chapter we develop the multinomial naive Bayes prediction function, the incarnation of naive Bayes for categorical predictors. We develop the function from its mathematical foundation before applying it to two very different problems: predicting the authorship of the Federalist Papers and a problem from the business marketing domain—classifying shoppers based on their grocery store purchases. The Federalist Papers application provides the opportunity to work with textual data.

This chapter delves into the key mathematical and computational components of data analytic algorithms. The purpose of these algorithms is to reduce massively large data sets to much smaller data sets with a minimal loss of relevant information. From the mathematical perspective, a data reduction algorithm is a sequence of data mappings, that is, functions that consume data in the form of sets and output data in a reduced form. The mathematical perspective is important because it imposes certain desirable attributes on the mappings. However, most of our attention is paid to the practical aspects of turning the mappings into code. The mathematical and computational aspects of data mappings are applied through the use of data dictionaries. The tutorials of this chapter help the reader develop familiarity with data mappings and Python dictionaries.

Linear regression is a broad and well-developed area of statistics. If there is a core to statistical methodology, then linear regression is it. The ubiquity of linear regression methods in statistics and data analytics stems from the ease with which one may fit tractable models that describe the primary features of a process or population. Not only is linear regression useful for description, it’s also very useful for prediction since the models often provide good approximations of complex relationships. In the field of statistics, hypothesis testing and confidence intervals are routinely used in linear regression analyses. The extension of these methods to data science is often unsuccessful because of the prevalence of opportunistically collected data. Most of the time, opportunistically collected data cannot support inferential methods because the quality of the inferences produced by the methods is unknown. We discuss inference herein so that the reader may understand the potential for success and for failure of these methods. However, the focus is on the essential and most useful aspects of the subject matter for data analytics—the fitted models. The topic of linear regression provides an avenue to gain experience with the statistical package R, one of the most popular software packages used by data scientists.

Purpose The acquisition cost of commercially-available (CA) dialysis solutions is higher than manually-compounded (MC) solutions but pharmacy preparation time and medication errors are presumed to be lower. The objective of this study was to compare resource utilization and total cost between CA- and MC-dialysis solutions. Methods A model was constructed using solution acquisition cost, preparation cost, and the cost of a medication error. Solution preparation costs were obtained by prospectively recording preparation times for 1 month before and after the implementation of a CA-solution protocol. Preparation times were converted to costs using the hourly wage of a pharmacist and pharmacy technician. The presumed error rate was 0.5% for MC and 0.1% for CA solutions. A univariate sensitivity analysis was conducted by varying the incidence of a medication error. Results Preparation time for an MC solution was significantly longer than for a CA solution (8 ± 3 minutes/bag vs 4 ± 1 minutes/bag, P < .001). Total solution costs (acquisition + preparation) were $11.13 and $26.38 per bag for MC and CA, respectively. When medication errors were considered, the cost per error-free event was $48.14 per bag for MC solutions and $33.77 per bag for CA solutions. The threshold where the cost-ratio became less costly for MC solutions was when the absolute difference in error rate was no more than 0.2%. Conclusion Despite a higher acquisition cost, CA solutions could result in lower total costs compared with MC solutions. A small difference in medication error rate is required to produce these savings.

Fire frequency and severity, and vegetation composition and structure have been altered across much of North America during the past century because of fire exclusion and other land management practices. The cumulative results are now recognized to be partly responsible for dramatic increases in wildland fire severity and declines in ecosystem health. In response, the Departments of Agriculture and Interior are developing a strategy for reducing fire hazard and restoring forest health. A key component of this strategy is the assessment of the departure of current plant communities from historical conditions. Assessing departure is difficult because of limited spatial coverage of data on successional class distribution prior to European settlement (historical) conditions. This article discusses a two-step approach to the problem that first generates data on historical vegetation composition and structure by simulating succession and disturbance processes under historical conditions, and then measures departure by comparing these simulated data to an observation on current conditions. The data are observations on high-dimensional multinomial categorical variables, and pose several problems that limit the usefulness of conventional statistical methods. We propose a method that constructs a linear approximation of the current observation vector from the simulated historic data, and measures the departure of the current observation vector by the length of the residual error vector. A simulation study indicates that this departure measure is substantially more sensitive than conventional outlier detection methods. Our methodology is demonstrated using a pilot region encompassing lands in Utah.

Fire can cause profound changes in the composition and abundance of plant and animal species, but logistics, unpredictability of weather, and inherent danger make it nearly impossible to study high-severity fire effects experimentally. We took advantage of a unique opportunity to use a before-after/control-impact (BACI) approach to analyze changes in bird assemblages after the severe fires of 2000 in the Bitterroot Valley, Montana. Observers surveyed birds using 10-minute point counts and collected vegetation data from 13 burned and 13 unburned transects for five years before fire and three years after fire. We compared changes in vegetation variables and relative bird abundance from before to after fire between the set of points that burned and the set of points that did not burn. The magnitude of change in vegetation variables from before to after fire increased with fire severity. The relative abundances of nine bird species showed significantly greater changes from before to after fire at burned points compared with unburned points. Moreover, when burned points were separated by whether they burned at low, moderate, or high severity, an additional 10 species showed significant changes in relative abundance from before to after fire at one or more severities. Overall, almost twice as many bird species increased as decreased significantly in response to fire. We also found changes in abundance between one year after and two years after fire for most species that responded to fire. Thus, species that have been termed ''mixed responders'' in the literature appear to be responding dif- ferently to different fire severities or different time periods since fire, rather than responding variably to the same fire conditions. These findings underscore the importance of fire severity and time since fire and imply that both factors must be considered to understand the complexities of fire effects on biological communities. Because different bird species re- sponded positively to different fire severities, our results suggest a need to manage public lands for the maintenance of all kinds of fires, not just the low-severity, understory burns that dominate most discussions revolving around the use of fire in forest restoration.

We sought to determine whether a novel method of placebo acupuncture can be differentiated by subjects from real acupuncture treatment. A single-blind, randomized, controlled clinical trial with an independent observer was performed. Forty-nine healthy subjects over the age of 18 years were randomly assigned to one of 2 experimental groups: 24 subjects received real acupuncture, and 25 subjects received placebo acupuncture. Placebo acupuncture was performed by administering a blunted acupuncture needle through a foam pad at the Large Intestine 4 acupoint. The blunted needle touched but did not penetrate the skin. Real acupuncture was performed by administering an acupuncture needle through a foam pad at the Large Intestine 4 acupoint. The needle pricked and penetrated the skin to a depth of 10 to 20 mm. A simple questionnaire followed, asking whether the subject believed they received real or placebo acupuncture. Twenty-two (88%) of the 25 subjects who received placebo acupuncture believed they received real acupuncture. Nineteen (79.2%) of the 24 subjects who received real acupuncture correctly determined they received real acupuncture. The Fisher exact test showed an insignificant difference between real and placebo acupuncture treatments (P = .463). Subjects were not able to differentiate between real or placebo acupuncture, thereby validating this novel method of administering placebo acupuncture as a good control for acupuncture-naive patients. Perspective The method of placebo acupuncture herein described is a valid control for acupuncture research involving acupuncture-naive patients.

This is an important collection of essays on Thomas Paine. Many of them consider Paine's relationship to Thomas Jefferson, and the two remain inseparable in this collection because, as Edward G. Gr...

The time and effort required of probability sampling for accuracy assessment of large-scale land cover maps often means that probability test samples are not collected. Yet, map usefulness is substantially reduced without reliable accuracy estimates. In this article, we introduce a method of estimating the accuracy of a classified map that does not utilize a test sample in the usual sense, but instead estimates the probability of correct classification for each map unit using only the classification rule and the map unit covariates. We argue that the method is an improvement over conventional estimators, though it does not eliminate the need for probability sampling. The method also provides a new and simple method of constructing accuracy maps. We illustrate some of problems associated with accuracy assessment of broad-scale land cover maps, and our method, with a set of nine Landsat Thematic Mapper satellite image-based land cover maps from Montana and Wyoming, USA.

There is abundant evidence that thematic map accuracy may vary across a landscape in a manner that is only partially related to land cover type. Spatial variation in accuracy may be attributable to factors such as terrain, landscape complexity, and land use patterns. There may be serious consequences in the construction and application of thematic maps when the pattern and degree of spatial variation of map accuracy is not understood. Classification error matrices, the usual summary of thematic map accuracy, are not a natural tool for analyzing spatial variation in accuracy. This article presents a method of mapping spatially based estimates of map accuracy. We formulate a concept of misclassification probability and present a method of estimating misclassification probabilities at training observation locations. Misclassification probability estimates are then interpolated from the training locations to a lattice of points via Kriging. The last step uses the lattice to construct a contour accuracy map. We illustrate the method by constructing an accuracy map for a full Landsat Thematic Mapper scene covering a portion of central Idaho and western Montana, and a finer resolution accuracy map for a small area within that scene. The method is found to provide valuable information on the spatial distribution and variation of map accuracy for both decision-makers and GIS analysts.

Populations of arctic-alpine plants at the southern periphery of their range should respond rapidly to projected global warming. We established a study to monitor the density and reproductive effort of six such species in tundra of Glacier National Park, Montana to help project the fate of these species in the center of their range. For many species, detecting long-term population trends is confounded by short-term variation. Our study design employs temporal resampling of permanent plots on multiple sites and a repeated-measures model that accommodates the effects of high frequency variation and allows assessment of the significance of long-term trends. Statistical analysis compares site-specific estimates of annual mean density between two time periods and uses between- plot, within-site, within-year variation to estimate error. Power calculations using data from the 3-yr baseline period indicate that measurements of fecundity will be less sensitive for detecting long-term trends than measurements of plant density. Furthermore, our results suggest that perennial species of closed-turf communities may be better bioassays of long- term change than annuals or species of open, ephemeral microsites.

This book emphasises the centrality of nationhood to Thomas Jefferson's thought and politics, envisioning Jefferson as a cultural nationalist whose political project sought the alignment of the American state system with the will and character of the nation. Jefferson believed that America was the one nation on earth able to realise in practice universal ideals to which other peoples could only aspire. He appears in the book as the essential narrator of what he once called the 'American Story': as the historian, the sociologist and the ethnographer; the political theorist of the nation; the most successful practitioner of its politics; and its most enthusiastic champion. The book argues that reorienting Jefferson around the concept of American nationhood recovers an otherwise easily missed coherence to his political career and helps make sense of a number of conundrums in his thought and practice.

Some manufacturing, research, and development conducted within AT&T involves processes that emit air contaminants. These emissions are confined, scrubbed to recover the constituents, and vented outside through stacks. Continuous emission monitoring (CEM) systems analyze the emissions in near-real time and provide feedback to control them. The information collected from CEM systems in some industries is used to prove compliance with applicable environmental regulations. This paper describes CEM technology and an Environmental Monitoring and Reporting System (EMARS) for near-real-time emissions monitoring. It also presents selected applications in AT&T Bell Laboratories and AT&T manufacturing facilities at Montgomery and Richmond.