What\u27s in a Name? New Bacterial Species and Changes to Taxonomic Status from 2012 through 2015

Technological advancements in fields such as molecular genetics and the human microbiome have resulted in an unprecedented recognition of new bacterial genus/species designations by the International Journal of Systematic and Evolutionary Microbiology. Knowledge of designations involving clinically significant bacterial species would benefit clinical microbiologists in the context of emerging pathogens, performance of accurate organism identification, and antimicrobial susceptibility testing. In anticipation of subsequent taxonomic changes being compiled by the Journal of Clinical Microbiology on a biannual basis, this compendium summarizes novel species and taxonomic revisions specific to bacteria derived from human clinical specimens from the calendar years 2012 through 2015

The Development and Evaluation of a Seminar for Teachers on the Awareness and Management of Allergic Conditions of their Students in the Classroom

Problem. Many children with allergic problems go without treatment because their problems go unrecognized by both their parents and teachers. The purpose of this project was to bring about change on the part of teachers enabling them better to address allergic problems in their students. Literature was reviewed with respect to allergies and with respect to the use of inservice seminars for teachers. The posttest means for all three areas of measurement of the seminar group and control group were adjusted for pretest scores by analysis of covariance which indicated a significant difference in favor of the seminar group in each area. Conclusion. With the attainment of significance in most areas of this research study the teachers demonstrated that they could benefit from training in the area of children\u27s health problems. Recommendations were made to reduce teacher resistance to the seminar by relating health problems and by involving a team of specialists in the presentations

Primary task event-related potentials related to different aspects of information processing

The results of two studies which investigated the relationships between cognitive processing and components of transient event-related potentials (ERPs) are presented in a task in which mental workload was manipulated. The task involved the monitoring of an array of discrete readouts for values that went out of bounds, and was somewhat analogous to tasks performed in cockpits. The ERPs elicited by the changing readouts varied with the number of readouts being monitored, the number of monitored readouts that were close to going out of bounds, and whether or not the change took a monitored readout out of bounds. Moreover, different regions of the waveform differentially reflected these effects. The results confirm the sensitivity of scalp-recorded ERPs to the cognitive processes affected by mental workload and suggest the possibility of extracting useful ERP indices of primary task performance in a wide range of man-machine settings

Brain-wave measures of workload in advanced cockpits: The transition of technology from laboratory to cockpit simulator, phase 2

The present Phase 2 small business innovation research study was designed to address issues related to scalp-recorded event-related potential (ERP) indices of mental workload and to transition this technology from the laboratory to cockpit simulator environments for use as a systems engineering tool. The project involved five main tasks: (1) Two laboratory studies confirmed the generality of the ERP indices of workload obtained in the Phase 1 study and revealed two additional ERP components related to workload. (2) A task analysis' of flight scenarios and pilot tasks in the Advanced Concepts Flight Simulator (ACFS) defined cockpit events (i.e., displays, messages, alarms) that would be expected to elicit ERPs related to workload. (3) Software was developed to support ERP data analysis. An existing ARD-proprietary package of ERP data analysis routines was upgraded, new graphics routines were developed to enhance interactive data analysis, and routines were developed to compare alternative single-trial analysis techniques using simulated ERP data. (4) Working in conjunction with NASA Langley research scientists and simulator engineers, preparations were made for an ACFS validation study of ERP measures of workload. (5) A design specification was developed for a general purpose, computerized, workload assessment system that can function in simulators such as the ACFS

Understanding software faults and their role in software reliability modeling

This study is a direct result of an on-going project to model the reliability of a large real-time control avionics system. In previous modeling efforts with this system, hardware reliability models were applied in modeling the reliability behavior of this system. In an attempt to enhance the performance of the adapted reliability models, certain software attributes were introduced in these models to control for differences between programs and also sequential executions of the same program. As the basic nature of the software attributes that affect software reliability become better understood in the modeling process, this information begins to have important implications on the software development process. A significant problem arises when raw attribute measures are to be used in statistical models as predictors, for example, of measures of software quality. This is because many of the metrics are highly correlated. Consider the two attributes: lines of code, LOC, and number of program statements, Stmts. In this case, it is quite obvious that a program with a high value of LOC probably will also have a relatively high value of Stmts. In the case of low level languages, such as assembly language programs, there might be a one-to-one relationship between the statement count and the lines of code. When there is a complete absence of linear relationship among the metrics, they are said to be orthogonal or uncorrelated. Usually the lack of orthogonality is not serious enough to affect a statistical analysis. However, for the purposes of some statistical analysis such as multiple regression, the software metrics are so strongly interrelated that the regression results may be ambiguous and possibly even misleading. Typically, it is difficult to estimate the unique effects of individual software metrics in the regression equation. The estimated values of the coefficients are very sensitive to slight changes in the data and to the addition or deletion of variables in the regression equation. Since most of the existing metrics have common elements and are linear combinations of these common elements, it seems reasonable to investigate the structure of the underlying common factors or components that make up the raw metrics. The technique we have chosen to use to explore this structure is a procedure called principal components analysis. Principal components analysis is a decomposition technique that may be used to detect and analyze collinearity in software metrics. When confronted with a large number of metrics measuring a single construct, it may be desirable to represent the set by some smaller number of variables that convey all, or most, of the information in the original set. Principal components are linear transformations of a set of random variables that summarize the information contained in the variables. The transformations are chosen so that the first component accounts for the maximal amount of variation of the measures of any possible linear transform; the second component accounts for the maximal amount of residual variation; and so on. The principal components are constructed so that they represent transformed scores on dimensions that are orthogonal. Through the use of principal components analysis, it is possible to have a set of highly related software attributes mapped into a small number of uncorrelated attribute domains. This definitively solves the problem of multi-collinearity in subsequent regression analysis. There are many software metrics in the literature, but principal component analysis reveals that there are few distinct sources of variation, i.e. dimensions, in this set of metrics. It would appear perfectly reasonable to characterize the measurable attributes of a program with a simple function of a small number of orthogonal metrics each of which represents a distinct software attribute domain

Correction for Munson and Carroll, “What’s in a Name? New Bacterial Species and Changes to Taxonomic Status from 2012 through 2015”

Author Correction