One-pot near-ambient temperature syntheses of aryl(difluoroenol) derivatives from trifluoroethanol

Difluoroalkenylzinc reagents prepared from 1-(2’-methoxy-ethoxymethoxy)-2,2,2-trifluoroethane and 1-(N,N-diethylcarbamoyloxy)-2,2,2-trifluoroethane at ice bath temperatures, underwent Negishi coupling with a range of aryl halides in a convenient one pot procedure. While significant differences between the enol acetal and carbamate reagents were revealed, the Negishi protocol compared very favourably with alternative coupling procedures in terms of overall yields from trifluoroethanol

The Accreditation Process for IS Programs in Business Schools

Accreditation is a seal of recognition sought by almost all institutions of higher education. Accreditation is offered at several levels, including university, college, school and program. American academicians are familiar with the Council for Higher Education Association and the Regional Accreditation\u27\u27 associations. Faculty in colleges and schools of business worldwide recognize AACSB International and European Quality Improvement System (EQUIS), as accrediting bodies for business colleges and schools. Similarly, degree programs, for example, accounting, the engineering fields, and health care professions, have accreditation bodies. One such accreditation body, very familiar to engineers and computer scientists, but new to business\u27 academicians is ABET. ABET, through its Computing Accreditation Commission (CAC), now accredits programs in Information Systems. This paper looks at the philosophy of accrediting programs, specifically information systems programs, overviews the ABET accreditation criteria and process, considers the costs and benefits\u27\u27 of accrediting information systems programs, and develops a diagram detailing the internal steps of the ABET accreditation process in a familiar systems analysis and design approach. These steps are then grouped into before, during, and after the accreditation onsite team visit. Potential impediments related to these steps are identified with resolution strategies are presented. The paper concludes by arguing that whether or not a program pursues IS accreditation, much of what is presented here and required of ABET is applicable to any well-run IS program dedicated to continuously delivering a quality curriculum to its students

Capturing accelerometer outputs in healthy volunteers under normal and simulated-pathological conditions using ML classifiers

Wearable devices offer a possible solution for acquiring objective measurements of physical activity. Most current algorithms are derived using data from healthy volunteers. It is unclear whether such algorithms are suitable in specific clinical scenarios, such as when an individual has altered gait. We hypothesized that algorithms trained on healthy population will result in less accurate results when tested in individuals with altered gait. We further hypothesized that algorithms trained on simulated-pathological gait would prove better at classifying abnormal activity.We studied healthy volunteers to assess whether activity classification accuracy differed for those with healthy and simulated-pathological conditions. Healthy participants (n=30) were recruited from the University of Leeds to perform nine predefined activities under healthy and simulated-pathological conditions. Activities were captured using a wrist-worn MOX accelerometer (Maastricht Instruments, NL). Data were analyzed based on the Activity-Recognition-Chain process. We trained a Neural-Network, Random-Forests, k-Nearest-Neighbors (k-NN), Support-Vector-Machines (SVM) and Naive Bayes models to classify activity. Algorithms were trained four times; once with 'healthy' data, and once with 'simulated-pathological data' for each of activity-type and activity-task classification. In activity-type instances, the SVM provided the best results; the accuracy was 98.4% when the algorithm was trained and then tested with unseen data from the same group of healthy individuals. Accuracy dropped to 52.8% when tested on simulated-pathological data. When the model was retrained with simulated-pathological data, prediction accuracy for the corresponding test set was 96.7%. Algorithms developed on healthy data are less accurate for pathological conditions. When evaluating pathological conditions, classifier algorithms developed using data from a target sub-population can restore accuracy to above 95%.Clinical Relevance - This method remotely establishes health-related data of objective outcome measures of activities of daily living

An Analysis of Drug Dissolution Rates in the USP 24 Type 2 Apparatus

This paper applies boundary layer theory to the process of drug dissolution in the USP 24, Type 2 Apparatus. The mass transfer rate from the top flat surface of a compact in various positions within the device is evaluated by means of a Pohlhausen integral method

Light Rare Earth Element Depletion During Deepwater Horizon Blowout Methanotrophy

Rare earth elements have generally not been thought to have a biological role. However, recent work has demonstrated that the light REEs (LREEs: La, Ce, Pr, and Nd) are essential for at least some methanotrophs, being co-factors in the XoxF type of methanol dehydrogenase (MDH). We show here that dissolved LREEs were significantly removed in a submerged plume of methane-rich water during the Deepwater Horizon (DWH) well blowout. Furthermore, incubation experiments conducted with naturally methane-enriched waters from hydrocarbon seeps in the vicinity of the DWH wellhead also showed LREE removal concurrent with methane consumption. Metagenomic sequencing of incubation samples revealed that LREE-containing MDHs were present. Our field and laboratory observations provide further insight into the biochemical pathways of methanotrophy during the DWH blowout. Additionally, our results are the first observations of direct biological alteration of REE distributions in oceanic systems. In view of the ubiquity of LREE-containing MDHs in oceanic systems, our results suggest that biological uptake of LREEs is an overlooked aspect of the oceanic geochemistry of this group of elements previously thought to be biologically inactive and an unresolved factor in the flux of methane, a potent greenhouse gas, from the ocean

Light Rare Earth Element Depletion During Deepwater Horizon Blowout Methanotrophy

Rare earth elements have generally not been thought to have a biological role. However, recent work has demonstrated that the light REEs (LREEs: La, Ce, Pr, and Nd) are essential for at least some methanotrophs, being co-factors in the XoxF type of methanol dehydrogenase (MDH). We show here that dissolved LREEs were significantly removed in a submerged plume of methane-rich water during the Deepwater Horizon (DWH) well blowout. Furthermore, incubation experiments conducted with naturally methane-enriched waters from hydrocarbon seeps in the vicinity of the DWH wellhead also showed LREE removal concurrent with methane consumption. Metagenomic sequencing of incubation samples revealed that LREE-containing MDHs were present. Our field and laboratory observations provide further insight into the biochemical pathways of methanotrophy during the DWH blowout. Additionally, our results are the first observations of direct biological alteration of REE distributions in oceanic systems. In view of the ubiquity of LREE-containing MDHs in oceanic systems, our results suggest that biological uptake of LREEs is an overlooked aspect of the oceanic geochemistry of this group of elements previously thought to be biologically inactive and an unresolved factor in the flux of methane, a potent greenhouse gas, from the ocean

A Multi-objective Exploratory Procedure for Regression Model Selection

Variable selection is recognized as one of the most critical steps in statistical modeling. The problems encountered in engineering and social sciences are commonly characterized by over-abundance of explanatory variables, non-linearities and unknown interdependencies between the regressors. An added difficulty is that the analysts may have little or no prior knowledge on the relative importance of the variables. To provide a robust method for model selection, this paper introduces the Multi-objective Genetic Algorithm for Variable Selection (MOGA-VS) that provides the user with an optimal set of regression models for a given data-set. The algorithm considers the regression problem as a two objective task, and explores the Pareto-optimal (best subset) models by preferring those models over the other which have less number of regression coefficients and better goodness of fit. The model exploration can be performed based on in-sample or generalization error minimization. The model selection is proposed to be performed in two steps. First, we generate the frontier of Pareto-optimal regression models by eliminating the dominated models without any user intervention. Second, a decision making process is executed which allows the user to choose the most preferred model using visualisations and simple metrics. The method has been evaluated on a recently published real dataset on Communities and Crime within United States.Comment: in Journal of Computational and Graphical Statistics, Vol. 24, Iss. 1, 201

Two-Loop Calculations with Vertex Corrections in the Walecka Model

Two-loop corrections with scalar and vector form factors are calculated for nuclear matter in the Walecka model. The on-shell form factors are derived from vertex corrections within the framework of the model and are highly damped at large spacelike momenta. The two-loop corrections are evaluated first by using the one-loop parameters and mean fields and then by refitting the total energy/baryon to empirical nuclear matter saturation properties. The modified two-loop corrections are significantly smaller than those computed with bare vertices. Contributions from the anomalous isoscalar form factor of the nucleon are included for the first time. The effects of the implicit density dependence of the form factors, which arise from the shift in the baryon mass, are also considered. Finally, necessary extensions of these calculations are discussed.Comment: 29 pages in REVTeX, 18 figures, preprint IU/NTC 94-02 //OSU--94-11

Vulnerability to bullying in children with a history of specific speech and language difficulties

This is an electronic version of an article published in Lindsay, Geoff and Dockrell, Julie and Mackie, Clare (2008) Vulnerability to bullying in children with a history of specific speech and language difficulties. European Journal of Special Needs Education, 23 (1). pp. 1-16. European Journal of Special Needs Education is available online at: http://www.informaworld.com/10.1080/0885625070179120