Models of care in geriatric oncology nursing

Objectives To review models of care for older adults with cancer, with a focus on the role of the oncology nurse in geriatric oncology care. International exemplars of geriatric oncology nursing care are discussed. Data source Published peer reviewed literature, web-based resources, professional society materials, and the authors' experience. Conclusion Nursing care for older patients with cancer is complex and requires integrating knowledge from multiple disciplines that blends the sciences of geriatrics, oncology, and nursing. and which recognizes the dimensions of quality of life. Implications for Nursing Practice: Oncology nurses can benefit from learning key skills of comprehensive geriatric screening and assessment to improve the care they provide for older adults with cancer.

Attributes of clinical role models as described by senior veterinary students in Australia

Role models incite admiration and provide inspiration, contributing to learning as students aspire to emulate their example. The attributes of physician role models for medical trainees are well documented, but they remain largely unexplored in the context of veterinary medical training. The aim of the current study was to describe the attributes that final-year veterinary students (N=213) at the University of Queensland identified when reflecting on their clinical role models. Clinical role model descriptions provided by students were analyzed using concept-mapping software (Leximancer v. 2.25). The most frequent and highly connected concepts used by students when describing their role model(s) included clients, vet, and animal. Role models were described as good communicators who were skilled at managing relationships with clients, patients, and staff. They had exemplary knowledge, skills, and abilities, and they were methodical and conducted well-structured consultations. They were well respected and, in turn, demonstrated respect for clients, colleagues, staff, and students alike. They were also good teachers and able to tailor explanations to suit both clients and students. Findings from this study may serve to assist with faculty development and as a basis for further research in this area.

Eliciting and encoding expert knowledge on variable selection into classical or Bayesian species distribution models

The quality of species distribution models (SDMs) relies to a large degree on the quality of the input data, from bioclimatic indices to environmental and habitat descriptors (Austin, 2002). Recent reviews of SDM techniques, have sought to optimize predictive performance e.g. Elith et al., 2006. In general SDMs employ one of three approaches to variable selection. The simplest approach relies on the expert to select the variables, as in environmental niche models Nix, 1986 or a generalized linear model without variable selection (Miller and Franklin, 2002). A second approach explicitly incorporates variable selection into model fitting, which allows examination of particular combinations of variables. Examples include generalized linear or additive models with variable selection (Hastie et al. 2002); or classification trees with complexity or model based pruning (Breiman et al., 1984, Zeileis, 2008). A third approach uses model averaging, to summarize the overall contribution of a variable, without considering particular combinations. Examples include neural networks, boosted or bagged regression trees and Maximum Entropy as compared in Elith et al. 2006. Typically, users of SDMs will either consider a small number of variable sets, via the first approach, or else supply all of the candidate variables (often numbering more than a hundred) to the second or third approaches. Bayesian SDMs exist, with several methods for eliciting and encoding priors on model parameters (see review in Low Choy et al. 2010). However few methods have been published for informative variable selection; one example is Bayesian trees (O’Leary 2008). Here we report an elicitation protocol that helps makes explicit a priori expert judgements on the quality of candidate variables. This protocol can be flexibly applied to any of the three approaches to variable selection, described above, Bayesian or otherwise. We demonstrate how this information can be obtained then used to guide variable selection in classical or machine learning SDMs, or to define priors within Bayesian SDMs.

Mathematical models of calcium and tight junctions in normal and reconstructed epidermis

This project investigated the calcium distributions of the skin, and the growth patterns of skin substitutes grown in the laboratory, using mathematical models. The research found that the calcium distribution in the upper layer of the skin is controlled by three different mechanisms, not one as previously thought. The research also suggests that tight junctions, which are adhesions between neighbouring skin cells, cannot be solely responsible for the differences in the growth patterns of skin substitutes and normal skin.

Product-library-derived specification parameters in building information models augmentation

This paper conceptualizes a framework for bridging the BIM (building information modelling)-specifications divide through augmenting objects within BIM with specification parameters derived from a product library. We demonstrate how model information, enriched with data at various LODs (levels of development), can evolve simultaneously with design and construction using different representation of a window object embedded in a wall as lifecycle phase exemplars at different levels of granularity. The conceptual standpoint is informed by the need for exploring a methodological approach which extends beyond current limitations of current modelling platforms in enhancing the information content of BIM models. Therefore, this work demonstrates that BIM objects can be augmented with construction specification parameters leveraging product libraries.

Framing as status or benefits? Consumers’ reactions to hierarchical loyalty program communication

Purpose A fundamental aspect of hierarchical loyalty programs is that some consumers get rewards that others do not. Despite the widespread use of such programs, academics have long debated whether these benefits are outweighed by the potential negative impact of the differential treatment of customers. This study extends our understanding, examining the impact of message framing on consumers’ reactions to hierarchical loyalty structures. Design/methodology/approach Three online studies were conducted. Study 1 uses advertisements to manipulate the message frame’s emphasis (benefits vs. status). Study 2 manipulates consumers’ frame of thought by directing their attention to either changes in benefits or status. Finally, Study 3 uses the proposed framework to reconcile contradictory findings from past research. Findings Low-frequency customers who do not expect to qualify for a superior customer tier tend to reject hierarchical programs when thinking about status. In contrast, when these customers think about concrete rewards, loyalty program messages produce no negative reactions. High-frequency customers are positively affected by communication regardless of the type of benefits framed. Research limitations/implications All studies were done online potentially limiting the external validity of the results. Nevertheless, the impact of message framing on perceptions about the loyalty program seems to be quite robust across different studies and manipulations. Practical implications When communicating with low-frequency customers managers should avoid promising status; customers should instead be motivated based on concrete rewards. High-frequency customers are indifferent to alternative emphasis of communication frames. Originality/value Marketing academics have acknowledged the importance of being able to reward top customers without demotivating light and moderate users. Our research is the first to provide a solution to this issue.

Paraxial schematic eye models for 7- and 14-year-old Chinese children

Purpose.: To develop three-surface paraxial schematic eyes with different ages and sexes based on data for 7- and 14-year-old Chinese children from the Anyang Childhood Eye Study. Methods.: Six sets of paraxial schematic eyes, including 7-year-old eyes, 7-year-old male eyes, 7-year-old female eyes, 14-year-old eyes, 14-year-old male eyes, and 14-year-old female eyes, were developed. Both refraction-dependent and emmetropic eye models were developed, with the former using linear dependence of ocular parameters on refraction. Results.: A total of 2059 grade 1 children (boys 58%) and 1536 grade 8 children (boys 49%) were included, with mean age of 7.1 ± 0.4 and 13.7 ± 0.5 years, respectively. Changes in these schematic eyes with aging are increased anterior chamber depth, decreased lens thickness, increased vitreous chamber depth, increased axial length, and decreased lens equivalent power. Male schematic eyes have deeper anterior chamber depth, longer vitreous chamber depth, longer axial length, and lower lens equivalent power than female schematic eyes. Changes in the schematic eyes with positive increase in refraction are decreased anterior chamber depth, increased lens thickness, decreased vitreous chamber depth, decreased axial length, increased corneal radius of curvature, and increased lens power. In general, the emmetropic schematic eyes have biometric parameters similar to those arising from regression fits for the refraction-dependent schematic eyes. Conclusions.: The paraxial schematic eyes of Chinese children may be useful for myopia research and for facilitating comparison with other children with the same or different racial backgrounds and living in different places.

Monitoring healing progression and characterizing the mechanical environment in preclinical models for bone tissue engineering

The treatment of large segmental bone defects remains a significant clinical challenge. Due to limitations surrounding the use of bone grafts, tissue-engineered constructs for the repair of large bone defects could offer an alternative. Before translation of any newly developed tissue engineering (TE) approach to the clinic, efficacy of the treatment must be shown in a validated preclinical large animal model. Currently, biomechanical testing, histology, and microcomputed tomography are performed to assess the quality and quantity of the regenerated bone. However, in vivo monitoring of the progression of healing is seldom performed, which could reveal important information regarding time to restoration of mechanical function and acceleration of regeneration. Furthermore, since the mechanical environment is known to influence bone regeneration, and limb loading of the animals can poorly be controlled, characterizing activity and load history could provide the ability to explain variability in the acquired data sets and potentially outliers based on abnormal loading. Many approaches have been devised to monitor the progression of healing and characterize the mechanical environment in fracture healing studies. In this article, we review previous methods and share results of recent work of our group toward developing and implementing a comprehensive biomechanical monitoring system to study bone regeneration in preclinical TE studies.

Evaluation of k-epsilon and RNG turbulence models for confined swirling and non-swirling flow

In a very recent study [1] the Renormalisation Group (RNG) turbulence model was used to obtain flow predictions in a strongly swirling quarl burner, and was found to perform well in predicting certain features that are not well captured using less sophisticated models of turbulence. The implication is that the RNG approach should provide an economical and reliable tool for the prediction of swirling flows in combustor and furnace geometries commonly encountered in technological applications. To test this hypothesis the present work considers flow in a model furnace for which experimental data is available [2]. The essential features of the flow which differentiate it from the previous study [1] are that the annular air jet entry is relatively narrow and the base wall of the cylindrical furnace is at 90 degrees to the inlet pipe. For swirl numbers of order 1 the resulting flow is highly complex with significant inner and outer recirculation regions. The RNG and standard k-epsilon models are used to model the flow for both swirling and non-swirling entry jets and the results compared with experimental data [2]. Near wall viscous effects are accounted for in both models via the standard wall function formulation [3]. For the RNG model, additional computations with grid placement extending well inside the near wall viscous-affected sublayer are performed in order to assess the low Reynolds number capabilities of the model.

Computation of confined swirling flow using RNG and k-epsilon turbulence models

In this work we numerically model isothermal turbulent swirling flow in a cylindrical burner. Three versions of the RNG k-epsilon model are assessed against performance of the standard k-epsilon model. Sensitivity of numerical predictions to grid refinement, differing convective differencing schemes and choice of (unknown) inlet dissipation rate, were closely scrutinised to ensure accuracy. Particular attention is paid to modelling the inlet conditions to within the range of uncertainty of the experimental data, as model predictions proved to be significantly sensitive to relatively small changes in upstream flow conditions. We also examine the characteristics of the swirl--induced recirculation zone predicted by the models over an extended range of inlet conditions. Our main findings are: - (i) the standard k-epsilon model performed best compared with experiment; - (ii) no one inlet specification can simultaneously optimize the performance of the models considered; - (iii) the RNG models predict both single-cell and double-cell IRZ characteristics, the latter both with and without additional internal stagnation points. The first finding indicates that the examined RNG modifications to the standard k-e model do not result in an improved eddy viscosity based model for the prediction of swirl flows. The second finding suggests that tuning established models for optimal performance in swirl flows a priori is not straightforward. The third finding indicates that the RNG based models exhibit a greater variety of structural behaviour, despite being of the same level of complexity as the standard k-e model. The plausibility of the predicted IRZ features are discussed in terms of known vortex breakdown phenomena.

Carbon-coated hierarchical SnO2 hollow spheres for lithium ion batteries

Hierarchical SnO2 hollow spheres self-assembled from nanosheets were prepared with and without carbon coating. The combination of nanosized architecture, hollow structure, and a conductive carbon layer endows the SnO2-based anode with improved specific capacity and cycling stability, making it more promising for use in lithium ion batteries.

3D hierarchical rutile TiO2 and metal-free organic sensitizer producing dye-sensitized solar cells 8.6% conversion efficiency

Three-dimensional (3D) hierarchical nanoscale architectures comprised of building blocks, with specifically engineered morphologies, are expected to play important roles in the fabrication of 'next generation' microelectronic and optoelectronic devices due to their high surface-to-volume ratio as well as opto-electronic properties. Herein, a series of well-defined 3D hierarchical rutile TiO2 architectures (HRT) were successfully prepared using a facile hydrothermal method without any surfactant or template, simply by changing the concentration of hydrochloric acid used in the synthesis. The production of these materials provides, to the best of our knowledge, the first identified example of a ledgewise growth mechanism in a rutile TiO2 structure. Also for the first time, a Dye-sensitized Solar Cell (DSC) combining a HRT is reported in conjunction with a high-extinction-coefficient metal-free organic sensitizer (D149), achieving a conversion efficiency of 5.5%, which is superior to ones employing P25 (4.5%), comparable to state-of-the-art commercial transparent titania anatase paste (5.8%). Further to this, an overall conversion efficiency 8.6% was achieved when HRT was used as the light scattering layer, a considerable improvement over the commercial transparent/reflector titania anatase paste (7.6%), a significantly smaller gap in performance than has been seen previously.

Robust superhydrophobicity of hierarchical ZnO hollow microspheres fabricated by two-step self-assembly

Superhydrophobic and superhydrophilic surfaces have been extensively investigated due to their importance for industrial applications. It has been reported, however, that superhydrophobic surfaces are very sensitive to heat, ultraviolet (UV) light, and electric potential, which interfere with their long-term durability. In this study, we introduce a novel approach to achieve robust superhydrophobic thin films by designing architecture-defined complex nanostructures. A family of ZnO hollow microspheres with controlled constituent architectures in the morphologies of 1D nanowire networks, 2D nanosheet stacks, and 3D mesoporous nanoball blocks, respectively, was synthesized via a two-step self-assembly approach, where the oligomers or the constituent nanostructures with specially designed structures are first formed from surfactant templates, and then further assembled into complex morphologies by the addition of a second co-surfactant. The thin films composed of two-step synthesized ZnO hollow microspheres with different architectures presented superhydrophobicities with contact angles of 150°-155°, superior to the contact angle of 103° for one-step synthesized ZnO hollow microspheres with smooth and solid surfaces. Moreover, the robust superhydrophobicity was further improved by perfluorinated silane surface modification. The perfluorinated silane treated ZnO hollow microsphere thin films maintained excellent hydrophobicity even after 75 h of UV irradiation. The realization of environmentally durable superhydrophobic surfaces provides a promising solution for their long-term service under UV or strong solar light irradiations.

Concise review: Humanized models of tumor immunology in the 21st century: Convergence of cancer research and tissue engineering

Despite positive testing in animal studies, more than 80% of novel drug candidates fail to proof their efficacy when tested in humans. This is primarily due to the use of preclinical models that are not able to recapitulate the physiological or pathological processes in humans. Hence, one of the key challenges in the field of translational medicine is to “make the model organism mouse more human.” To get answers to questions that would be prognostic of outcomes in human medicine, the mouse's genome can be altered in order to create a more permissive host that allows the engraftment of human cell systems. It has been shown in the past that these strategies can improve our understanding of tumor immunology. However, the translational benefits of these platforms have still to be proven. In the 21st century, several research groups and consortia around the world take up the challenge to improve our understanding of how to humanize the animal's genetic code, its cells and, based on tissue engineering principles, its extracellular microenvironment, its tissues, or entire organs with the ultimate goal to foster the translation of new therapeutic strategies from bench to bedside. This article provides an overview of the state of the art of humanized models of tumor immunology and highlights future developments in the field such as the application of tissue engineering and regenerative medicine strategies to further enhance humanized murine model systems.

Multi-GNSS precise point positioning with raw single-frequency and dual-frequency measurement models

The emergence of multiple satellite navigation systems, including BDS, Galileo, modernized GPS, and GLONASS, brings great opportunities and challenges for precise point positioning (PPP). We study the contributions of various GNSS combinations to PPP performance based on undifferenced or raw observations, in which the signal delays and ionospheric delays must be considered. A priori ionospheric knowledge, such as regional or global corrections, strengthens the estimation of ionospheric delay parameters. The undifferenced models are generally more suitable for single-, dual-, or multi-frequency data processing for single or combined GNSS constellations. Another advantage over ionospheric-free PPP models is that undifferenced models avoid noise amplification by linear combinations. Extensive performance evaluations are conducted with multi-GNSS data sets collected from 105 MGEX stations in July 2014. Dual-frequency PPP results from each single constellation show that the convergence time of undifferenced PPP solution is usually shorter than that of ionospheric-free PPP solutions, while the positioning accuracy of undifferenced PPP shows more improvement for the GLONASS system. In addition, the GLONASS undifferenced PPP results demonstrate performance advantages in high latitude areas, while this impact is less obvious in the GPS/GLONASS combined configuration. The results have also indicated that the BDS GEO satellites have negative impacts on the undifferenced PPP performance given the current “poor” orbit and clock knowledge of GEO satellites. More generally, the multi-GNSS undifferenced PPP results have shown improvements in the convergence time by more than 60 % in both the single- and dual-frequency PPP results, while the positioning accuracy after convergence indicates no significant improvements for the dual-frequency PPP solutions, but an improvement of about 25 % on average for the single-frequency PPP solutions.