Understanding The Technology Support Needs Of High School Teachers Implementing A Learning Management System

In this study, the researcher explored high school teachers’ perceptions of various types of professional development, training, and support provided to and received by them during the implementation of a learning management system. Additionally, the researcher sought to explore the impact support may have on teachers’ beliefs about technology and its role/impact on their instructional practice. Data were collected on teachers’ perceptions of technology, as well as the most effective types of support (and frequency) they recalled were most useful for their respective skill level with technology. To identify participants’ skill level with technology, the Survey of Preservice Teachers\u27 Knowledge of Teaching and Technology (TPACK survey) developed by Schmidt et al. (2009) was used. The survey was based on the technological pedagogical and content knowledge (TPACK) framework. This study also utilized the models of continuing professional development (CPD), defined by Kennedy (2005) as a framework to help classify and organize the numerous and varied types of technology professional development (PD), training, and support received during the implementation of the learning management system (LMS). Findings from this study highlighted the varying support needs of teachers based on technology skill and draw a connection between teacher technology skill level and teacher beliefs about technology’s role in instructional practice and student learning

Active Investing in Strategic Acquirers Using an EVA Style Analysis

Employing an EVA style classification, we examine whether active investors (such as hedge funds and other long-short investors) can develop an alpha-generating strategy by classifying acquisitions based on the pre-acquisition EVA style quadrant of the acquirers. Over a recent ten-year period, the announcement evidence suggests that acquisitions across all style quadrants generate negative risk-adjusted returns: wherein the magnitude of economic gains from shorting acquirers is determined by EVA style characteristics; namely wealth creators or wealth destroyers. Moreover, we find that the potential for longing gains on targets of acquiring firms is also captured by EVA style

Tactical Asset Allocation and Presidential Elections

We analyze tactical asset allocation decisions around presidential elections using traditional methodology and then in the context of an efficient frontier analysis rather than the traditional stock-only or bond-only allocations in prior literature. To our knowledge, this is the first paper in the literature that addresses asset returns around presidential elections in a mean-variance efficient frontier framework. We find that the efficient frontier is sensitive to presidential time periods, with Democrats providing the best risk-reward opportunities over the long term, while Republicans provide better opportunities over the past quarter century

Predicting Insulin Pump Therapy Settings

Millions of people live with diabetes worldwide [7]. To mitigate some of the many symptoms associated with diabetes, an estimated 350,000 people in the United States rely on insulin pumps [17]. For many of these people, how effectively their insulin pump performs is the difference between sleeping through the night and a life threatening emergency treatment at a hospital. Three programmed insulin pump therapy settings governing effective insulin pump function are: Basal Rate (BR), Insulin Sensitivity Factor (ISF), and Carbohydrate Ratio (ICR). For many people using insulin pumps, these therapy settings are often not correct, given their physiological needs. While existing reinforcement learning models can predict actual physiological values for these settings, they require iteration and can be slow. The primary contribution of this research is to present a pipeline capable of providing instant predictions of close to actual patient physiological ISF, ICR, and BR from 30 days worth of data. In theory, this reduces patient waiting periods from roughly 6-8 weeks for existing reinforcement learning models to 30 days. This can serve as an aide in recommending pump therapy settings. Data used in this study include 1,000 simulated multivariate insulin pump time series. These time series were generated by a proprietary simulator developed by Tandem Diabetes Care. This multivariate time series data also integrates simulated continuous glucose monitor (CGM) data. This research proposes a pipeline for predicting actual patient BR, ISF, and ICR. Feature engineering, a component of this pipeline, included contextual consensus time series motif analysis. Models in the pipeline include time series native techniques such as Deep Convolutional Neural Networks (DNN) with a Long Short Term Memory input layers (LSTM) and aggregation based models such as Ridge regression and Lasso. Aggregation based ridge regression showed the most promising results, outperforming a naive model and a DNN model. For the data evaluated and with a 20% holdout test set, aggregate based ridge regression predicted the following normalized patient pump settings: ISF with a Mean Absolute Error of roughly 9.0%, ICR with a Mean Absolute Error of roughly 5% and BR with a Mean Absolute Error of roughly 6%. This is likely due to the reduction that aggregation based methods perform on each patient time series, reducing each one into a single tuple. This makes aggregation based methods less susceptible to noise and sparse signals. One limitation in this study is that the simulated data assumes a constant value of ISF, ICR, and BR over 24 hour periods for people with diabetes. In practice, this is not the case; ISF, ICR and BR fluctuate throughout the course of a day. A future consideration would be to use simulated data with non constant 24-hour ISF, ICR, and BR profiles. Insulin pumps greatly improve management and outcomes for people with diabetes. Ideally, by instantly improving programmed values of ISF, ICR, and BR, people relying on insulin pumps can spend less time worrying about their pump working ineffectively, and sleep through the night knowing it is less likely they will suffer a diabetes related medical emergency. To this end, it is the hope of the researchers that the ideas, pipelines, and inference presented are further explored and tested

Reconciling Semiclassical and Bohmian Mechanics: II. Scattering states for discontinuous potentials

In a previous paper [J. Chem. Phys. 121 4501 (2004)] a unique bipolar decomposition, Psi = Psi1 + Psi2 was presented for stationary bound states Psi of the one-dimensional Schroedinger equation, such that the components Psi1 and Psi2 approach their semiclassical WKB analogs in the large action limit. Moreover, by applying the Madelung-Bohm ansatz to the components rather than to Psi itself, the resultant bipolar Bohmian mechanical formulation satisfies the correspondence principle. As a result, the bipolar quantum trajectories are classical-like and well-behaved, even when Psi has many nodes, or is wildly oscillatory. In this paper, the previous decomposition scheme is modified in order to achieve the same desirable properties for stationary scattering states. Discontinuous potential systems are considered (hard wall, step, square barrier/well), for which the bipolar quantum potential is found to be zero everywhere, except at the discontinuities. This approach leads to an exact numerical method for computing stationary scattering states of any desired boundary conditions, and reflection and transmission probabilities. The continuous potential case will be considered in a future publication.Comment: 18 pages, 8 figure

Reconciling Semiclassical and Bohmian Mechanics: III. Scattering states for continuous potentials

In a previous paper [J. Chem. Phys. 121 4501 (2004)] a unique bipolar decomposition, Psi = Psi1 + Psi2 was presented for stationary bound states Psi of the one-dimensional Schroedinger equation, such that the components Psi1 and Psi2 approach their semiclassical WKB analogs in the large action limit. The corresponding bipolar quantum trajectories, as defined in the usual Bohmian mechanical formulation, are classical-like and well-behaved, even when Psi has many nodes, or is wildly oscillatory. A modification for discontinuous potential stationary stattering states was presented in a second paper [J. Chem. Phys. 124 034115 (2006)], whose generalization for continuous potentials is given here. The result is an exact quantum scattering methodology using classical trajectories. For additional convenience in handling the tunneling case, a constant velocity trajectory version is also developed.Comment: 16 pages and 14 figure

Identification and characterization of the interaction between the methyl-7-guanosine cap maturation enzyme RNMT and the cap-binding protein eIF4E

The control of RNA metabolism is an important aspect of molecular biology with wide-ranging impacts on cells. Central to processing of coding RNAs is the addition of the methyl-7 guanosine (m(7)G) “cap” on their 5’ end. The eukaryotic translation initiation factor eIF4E directly binds the m(7)G cap and through this interaction plays key roles in many steps of RNA metabolism including nuclear RNA export and translation. eIF4E also stimulates capping of many transcripts through its ability to drive the production of the enzyme RNMT which methylates the G-cap to form the mature m(7)G cap. Here, we found that eIF4E also physically associated with RNMT in human cells. Moreover, eIF4E directly interacted with RNMT in vitro. eIF4E is only the second protein reported to directly bind the methyltransferase domain of RNMT, the first being its co-factor RAM. We combined high-resolution NMR methods with biochemical studies to define the binding interfaces for the RNMT-eIF4E complex. Further, we found that eIF4E competes for RAM binding to RNMT and conversely, RNMT competes for binding of well-established eIF4E-binding partners such as the 4E-BPs. RNMT uses novel structural means to engage eIF4E. Finally, we observed that m(7)G cap-eIF4E-RNMT trimeric complexes form, and thus RNMT-eIF4E complexes may be employed so that eIF4E captures newly capped RNA. In all, we show for the first time that the cap-binding protein eIF4E directly binds to the cap-maturation enzyme RNMT

Community Support and Transition of Research to Operations for the Hurricane Weather Research and Forecasting Model

The Hurricane Weather Research and Forecasting Model (HWRF) is an operational model used to provide numerical guidance in support of tropical cyclone forecasting at the National Hurricane Center. HWRF is a complex multicomponent system, consisting of the Weather Research and Forecasting (WRF) atmospheric model coupled to the Princeton Ocean Model for Tropical Cyclones (POM-TC), a sophisticated initialization package including a data assimilation system and a set of postprocessing and vortex tracking tools. HWRF’s development is centralized at the Environmental Modeling Center of NOAA’s National Weather Service, but it incorporates contributions from a variety of scientists spread out over several governmental laboratories and academic institutions. This distributed development scenario poses significant challenges: a large number of scientists need to learn how to use the model, operational and research codes need to stay synchronized to avoid divergence, and promising new capabilities need to be tested for operational consideration. This article describes how the Developmental Testbed Center has engaged in the HWRF developmental cycle in the last three years and the services it provides to the community in using and developing HWRF

Compromised global embryonic transcriptome associated with advanced maternal age

Purpose To investigate the global transcriptome and associated embryonic molecular networks impacted with advanced maternal age (AMA). Methods Blastocysts derived from donor oocyte IVF cycles with no male factor infertility (< 30 years of age) and AMA blastocysts (≥ 42 years) with no other significant female factor infertility or male factor infertility were collected with informed patient consent. RNA sequencing libraries were prepared using the SMARTer® Ultra® Low Kit (Clontech Laboratories) and sequenced on the Illumina HiSEQ 4000. Bioinformatics included Ingenuity® Pathway Analysis (Qiagen) with ViiA™7 qPCR utilized for gene expression validation (Applied Biosystems). Results A total of 2688 significant differentially expressed transcripts were identified to distinguish the AMA blastocysts from young, donor controls. 2551 (95%) of these displayed decreased transcription in the blastocysts from older women. Pathway analysis revealed three altered molecular signaling networks known to be critical for embryo and fetal development: CREBBP, ESR1, and SP1. Validation of genes within these networks confirmed the global decreased transcription observed in AMA blastocysts (P < 0.05). Conclusions A significant, overall decreased global transcriptome was observed in blastocysts from AMA women. The ESR1/SP1/CREBBP pathway, in particular, was found to be a highly significant upstream regulator impacting biological processes that are vital during embryonic patterning and pre-implantation development. These results provide evidence that AMA embryos are compromised on a cell signaling level which can repress the embryo’s ability to proliferate and implant, contributing to a deterioration of reproductive outcomes

Linear response of mutans streptococci to increasing frequency of xylitol chewing gum use: a randomized controlled trial [ISRCTN43479664]

BACKGROUND: Xylitol is a naturally occurring sugar substitute that has been shown to reduce the level of mutans streptococci in plaque and saliva and to reduce tooth decay. It has been suggested that the degree of reduction is dependent on both the amount and the frequency of xylitol consumption. For xylitol to be successfully and cost-effectively used in public health prevention strategies dosing and frequency guidelines should be established. This study determined the reduction in mutans streptococci levels in plaque and unstimulated saliva to increasing frequency of xylitol gum use at a fixed total daily dose of 10.32 g over five weeks. METHODS: Participants (n = 132) were randomized to either active groups (10.32 g xylitol/day) or a placebo control (9.828 g sorbitol and 0.7 g maltitol/day). All groups chewed 12 pieces of gum per day. The control group chewed 4 times/day and active groups chewed xylitol gum at a frequency of 2 times/day, 3 times/day, or 4 times/day. The 12 gum pieces were evenly divided into the frequency assigned to each group. Plaque and unstimulated saliva samples were taken at baseline and five-weeks and were cultured on modified Mitis Salivarius agar for mutans streptococci enumeration. RESULTS: There were no significant differences in mutans streptococci level among the groups at baseline. At five-weeks, mutans streptococci levels in plaque and unstimulated saliva showed a linear reduction with increasing frequency of xylitol chewing gum use at the constant daily dose. Although the difference observed for the group that chewed xylitol 2 times/day was consistent with the linear model, the difference was not significant. CONCLUSION: There was a linear reduction in mutans streptococci levels in plaque and saliva with increasing frequency of xylitol gum use at a constant daily dose. Reduction at a consumption frequency of 2 times per day was small and consistent with the linear-response line but was not statistically significant