607 research outputs found
The Road to Researcher: The Development of Research Self-Efficacy in Higher Education Scholars
Aim/Purpose: Understanding how students develop a sense of efficacy as researchers can pro-vide faculty members in higher education doctoral programs insight into how to be more effective teachers and mentors, necessitating discipline-specific research on how graduate programs are and can be fostering students’ research self-efficacy (RSE). Thus, the purpose of this study was to explore how doctoral pro-grams and early research experiences contribute to the development of RSE in higher education scholars.
Background: Participants identified elements of the formal and “hidden” curriculumt pro-moted and inhibited RSE development.
Methodology: We employed multiple case study analysis of 17 individual early career scholars in higher education and student affairs.
Contribution: Findings indicate that the development of RSE is complex, but that Bandura’s four main sources of efficacy are a useful way to understand the types of experi-ences that students are and should be having to promote RSE. Our findings also highlight the importance of the research training environment in RSE develop-ment.
Findings: We found that the formal curriculum of participants’ doctoral programs – their research methods coursework and the process of writing their dissertations – were important facilitators of their RSE development. However, we also found that the “hidden curriculum” – the availability of extracurricular research oppor-tunities, faculty and peer mentoring, and the overall research culture of the doctoral programs – were influential in participants’ development.
Recommendations for Practitioners: Our findings point to a number of implications for higher education graduate programs seeking to improve students’ RSE. First, with regard to coursework, our findings point to the importance of recognizing the negative experiences students may bring with them to their doctoral programs, particularly related to quantita-tive methods, and of finding ways to help them see quantitative methods in dif-ferent ways than they have before. Second, our findings suggest important impli-cations for how faculty members as teachers, advisors, and mentors can think about providing feedback. Finally, our findings suggest the importance of under-standing the “hidden curriculum,” and how faculty members can influence stu-dents’ experiences outside of coursework and dissertations
Influence of copper on the electronic properties of amorphous chalcogenides
We have studied the influence of alloying copper with amorphous arsenic
sulfide on the electronic properties of this material. In our
computer-generated models, copper is found in two-fold near-linear and
four-fold square-planar configurations, which apparently correspond to Cu(I)
and Cu(II) oxidation states. The number of overcoordinated atoms, both arsenic
and sulfur, grows with increasing concentration of copper. Overcoordinated
sulfur is found in trigonal planar configuration, and overcoordinated
(four-fold) arsenic is in tetrahedral configuration. Addition of copper
suppresses the localization of lone-pair electrons on chalcogen atoms, and
localized states at the top of the valence band are due to Cu 3d orbitals.
Evidently, these additional Cu states, which are positioned at the same
energies as the states due to ([As4]-)-([S_3]+) pairs, are responsible for
masking photodarkening in Cu chalcogenides
The Infrared Imaging Spectrograph (IRIS) for TMT: the atmospheric dispersion corrector
We present a conceptual design for the atmospheric dispersion corrector (ADC)
for TMT's Infrared Imaging Spectrograph (IRIS). The severe requirements of this
ADC are reviewed, as are limitations to observing caused by uncorrectable
atmospheric effects. The requirement of residual dispersion less than 1
milliarcsecond can be met with certain glass combinations. The design decisions
are discussed and the performance of the design ADC is described. Alternative
options and their performance tradeoffs are also presented.Comment: SPIE Astronomical Instrumentation 201
Dynamical generalization of a solvable family of two-electron model atoms with general interparticle repulsion
Holas, Howard and March [Phys. Lett. A {\bf 310}, 451 (2003)] have obtained
analytic solutions for ground-state properties of a whole family of
two-electron spin-compensated harmonically confined model atoms whose different
members are characterized by a specific interparticle potential energy
u(). Here, we make a start on the dynamic generalization of the
harmonic external potential, the motivation being the serious criticism
levelled recently against the foundations of time-dependent density-functional
theory (e.g. [J. Schirmer and A. Dreuw, Phys. Rev. A {\bf 75}, 022513 (2007)]).
In this context, we derive a simplified expression for the time-dependent
electron density for arbitrary interparticle interaction, which is fully
determined by an one-dimensional non-interacting Hamiltonian. Moreover, a
closed solution for the momentum space density in the Moshinsky model is
obtained.Comment: 5 pages, submitted to J. Phys.
Undiagnosed metabolic syndrome and other adverse effects among clozapine users of Xhosa descent
Background. Clozapine use is known to be associated with significant side-effects, including prolongation of the QT-interval, agranulocytosis and metabolic syndrome. However, few data exist on the prevalence of clozapine side-effects in patients of Xhosa descent.Â
Objective. To gather data from Xhosa patients with schizophrenia to establish the prevalence of clozapine side-effects in this population.Â
Methods. Twenty-nine Xhosa patients with schizophrenia (as per the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR)) who had been receiving clozapine treatment for >1 year on an outpatient basis were selected for inclusion. All patients were participating in a genetics study in the Cape Metropolitan area. The participants were evaluated for the presence of side-effects (tests including an electrocardiogram, white blood cell count (WCC) and fasting blood glucose).Â
Results. The prevalence of metabolic syndrome was 44.8% (95% confidence interval (CI) 26.7 - 62.9) and of undiagnosed diabetes mellitus 13.8% (95% CI 1.24 - 26.34). There was a significant association between metabolic syndrome and body mass index (BMI) (p<0.01). The mean (SD) WCC was 7.8 Ă— 109/L (2.8), with 3.4% of the subjects having a WCC <3.5 Ă— 109/L. Sedation (82.8%; 95% CI 69.0 - 96.5), hypersalivation (79.3%; 95% CI 64.6 - 94.1) and constipation (44.8%; 95% CI 26.7 - 62.9) were common. The mean QT-interval was 373.8 (35.9) ms and 10% had a corrected QT-interval >440 ms. There was an association between the duration of clozapine treatment and QT-interval (with Bazett’s correction). Â
Conclusion. The high prevalence of metabolic syndrome and undiagnosed diabetes mellitus in this sample points to a need to monitor glucose levels and BMI on a regular basis. A larger study should be done to accurately quantify the differences in prevalence of side-effects between population groups
Protection of Pepper Plants from Drought by Microbacterium sp 3J1 by Modulation of the Plant's Glutamine and alpha-ketoglutarate Content: A Comparative Metabolomics Approach
Vilchez JI, Niehaus K, Dowling DN, Gonzalez-Lopez J, Manzanera M. Protection of Pepper Plants from Drought by Microbacterium sp 3J1 by Modulation of the Plant's Glutamine and alpha-ketoglutarate Content: A Comparative Metabolomics Approach. FRONTIERS IN MICROBIOLOGY. 2018;9: 17.Drought tolerance of plants such as tomato or pepper can be improved by their inoculation with rhizobacteria such as Microbacterium sp. 3J1. This interaction depends on the production of trehalose by the microorganisms that in turn modulate the phyto-hormone profile of the plant. In this work we describe the characterization of metabolic changes during the interaction of pepper plants with Microbacterium sp. 3J1 and of the microorganism alone over a period of drought. Our main findings include the observation that the plant responds to the presence of the microorganism by changing the C and N metabolism based on its glutamine and alpha-ketoglutarate content, these changes contribute to major changes in the concentration of molecules involved in the balance of the osmotic pressure. These include sugars and amino-acids; the concentration of antioxidant molecules, of metabolites involved in the production of phytohormones like ethylene, and of substrates used for lignin production such as ferulic and sinapic acids. Most of the altered metabolites of the plant when inoculated with Microbacterium sp. 3J1 in response to drought coincided with the profile of altered metabolites in the microorganism alone when subjected to drought, pointing to a response by which the plant relies on the microbe for the production of such metabolites. To our knowledge this is the first comparative study of the microbe colonized-plant and microbe alone metabolomes under drought stress
Quasiparticle energies for large molecules: a tight-binding GW approach
We present a tight-binding based GW approach for the calculation of
quasiparticle energy levels in confined systems such as molecules. Key
quantities in the GW formalism like the microscopic dielectric function or the
screened Coulomb interaction are expressed in a minimal basis of spherically
averaged atomic orbitals. All necessary integrals are either precalculated or
approximated without resorting to empirical data. The method is validated
against first principles results for benzene and anthracene, where good
agreement is found for levels close to the frontier orbitals. Further, the size
dependence of the quasiparticle gap is studied for conformers of the polyacenes
() up to n = 30.Comment: 10 pages, 5 eps figures submitted to Phys. Rev.
Quality control for more reliable integration of deep learning-based image segmentation into medical workflows
Machine learning algorithms underpin modern diagnostic-aiding software, whichhas proved valuable in clinical practice, particularly in radiology. However,inaccuracies, mainly due to the limited availability of clinical samples fortraining these algorithms, hamper their wider applicability, acceptance, andrecognition amongst clinicians. We present an analysis of state-of-the-artautomatic quality control (QC) approaches that can be implemented within thesealgorithms to estimate the certainty of their outputs. We validated the mostpromising approaches on a brain image segmentation task identifying whitematter hyperintensities (WMH) in magnetic resonance imaging data. WMH are acorrelate of small vessel disease common in mid-to-late adulthood and areparticularly challenging to segment due to their varied size, anddistributional patterns. Our results show that the aggregation of uncertaintyand Dice prediction were most effective in failure detection for this task.Both methods independently improved mean Dice from 0.82 to 0.84. Our workreveals how QC methods can help to detect failed segmentation cases andtherefore make automatic segmentation more reliable and suitable for clinicalpractice.<br
A semi-classical over-barrier model for charge exchange between highly charged ions and one-optical electron atoms
Absolute total cross sections for electron capture between slow, highly
charged ions and alkali targets have been recently measured. It is found that
these cross sections follow a scaling law with the projectile charge which is
different from the one previously proposed basing on a classical over-barrier
model (OBM) and verified using rare gases and molecules as targets. In this
paper we develop a "semi-classical" (i.e. including some quantal features) OBM
attempting to recover experimental results. The method is then applied to
ion-hydrogen collisions and compared with the result of a sophisticated
quantum-mechanical calculation. In the former case the accordance is very good,
while in the latter one no so satisfactory results are found. A qualitative
explanation for the discrepancies is attempted.Comment: RevTeX, uses epsf; 6 pages text + 3 EPS figures Journal of Physics B
(scehduled March 2000). This revision corrects fig.
How to predict relapse in leukemia using time series data: A comparative in silico study
Risk stratification and treatment decisions for leukemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improve the predictions for patient-specific treatment responses. We designed a synthetic experiment simulating response kinetics of 5,000 patients to compare different computational methods with respect to their ability to accurately predict relapse for chronic and acute myeloid leukemia treatment. Technically, we used clinical reference data to first fit a model and then generate de novo model simulations of individual patients’ time courses for which we can systematically tune data quality (i.e. measurement error) and quantity (i.e. number of measurements). Based hereon, we compared the prediction accuracy of three different computational methods, namely mechanistic models, generalized linear models, and deep neural networks that have been fitted to the reference data. Reaching prediction accuracies between 60 and close to 100%, our results indicate that data quality has a higher impact on prediction accuracy than the specific choice of the particular method. We further show that adapted treatment and measurement schemes can considerably improve the prediction accuracy by 10 to 20%. Our proof-of-principle study highlights how computational methods and optimized data acquisition strategies can improve risk assessment and treatment of leukemia patients
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