348 research outputs found
Studying Parallel Evolutionary Algorithms: The cellular Programming Case
Parallel evolutionary algorithms, studied to some extent over the past few years, have proven empirically worthwhileâthough there seems to be lacking a better understanding of their workings. In this paper we concentrate on cellular (fine-grained) models, presenting a number of statistical measures, both at the genotypic and phenotypic levels. We demonstrate the application and utility of these measures on a specific example, that of the cellular programming evolutionary algorithm, when used to evolve solutions to a hard problem in the cellular-automata domain, known as synchronization
Arctic air pollution: Challenges and opportunities for the next decade
The Arctic is a sentinel of global change. This region is influenced by multiple physical and socio-economic drivers and feedbacks, impacting both the natural and human environment. Air pollution is one such driver that impacts Arctic climate change, ecosystems and health but significant uncertainties still surround quantification of these effects. Arctic air pollution includes harmful trace gases (e.g. tropospheric ozone) and particles (e.g. black carbon, sulphate) and toxic substances (e.g. polycyclic aromatic hydrocarbons) that can be transported to the Arctic from emission sources located far outside the region, or emitted within the Arctic from activities including shipping, power production, and other industrial activities. This paper qualitatively summarizes the complex science issues motivating the creation of a new international initiative, PACES (air Pollution in the Arctic: Climate, Environment and Societies). Approaches for coordinated, international and interdisciplinary research on this topic are described with the goal to improve predictive capability via new understanding about sources, processes, feedbacks and impacts of Arctic air pollution. Overarching research actions are outlined, in which we describe our recommendations for 1) the development of trans-disciplinary approaches combining social and economic research with investigation of the chemical and physical aspects of Arctic air pollution; 2) increasing the quality and quantity of observations in the Arctic using long-term monitoring and intensive field studies, both at the surface and throughout the troposphere; and 3) developing improved predictive capability across a range of spatial and temporal scales
The preservation of erythrocytes at liquid nitrogen temperatures with hydroxyethyl starch: the removal of hydroxyethyl starch from erythrocytes after thawing
Packed human erythrocytes were frozen in liquid nitrogen using hydroxyethyl starch as a cryoprotective agent. Upon thawing, the cells were washed with a balanced salt solution. The washed cells were stable when resuspended in 0.154 n NaCl. The postthaw saline stability approximated the total recovery after washing the cells. Intracellular potassium loss was 15% immediately postthaw, and an additional 15% loss occurred after washing. These results indicate that HES can be removed from frozen-thawed cells by washing, and that freezing erythrocytes protected by HES does not result in significant losses of intracellular potassium.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/33516/1/0000014.pd
Identifying component modules
A computer-based system for modelling component dependencies and identifying component modules is presented. A variation of the Dependency Structure Matrix (DSM) representation was used to model component dependencies. The system utilises a two-stage approach towards facilitating the identification of a hierarchical modular structure. The first stage calculates a value for a clustering criterion that may be used to group component dependencies together. A Genetic Algorithm is described to optimise the order of the components within the DSM with the focus of minimising the value of the clustering criterion to identify the most significant component groupings (modules) within the product structure. The second stage utilises a 'Module Strength Indicator' (MSI) function to determine a value representative of the degree of modularity of the component groupings. The application of this function to the DSM produces a 'Module Structure Matrix' (MSM) depicting the relative modularity of available component groupings within it. The approach enabled the identification of hierarchical modularity in the product structure without the requirement for any additional domain specific knowledge within the system. The system supports design by providing mechanisms to explicitly represent and utilise component and dependency knowledge to facilitate the nontrivial task of determining near-optimal component modules and representing product modularity
Effects Of Length, Complexity, And Grammatical Correctness On Stuttering In Spanish-Speaking Preschool Children
Purpose: To explore the effects of utterance length, syntactic complexity, and grammatical correctness on stuttering in the spontaneous speech of young, monolingual Spanish-speaking children. Method: Spontaneous speech samples of 11 monolingual Spanish-speaking children who stuttered, ages 35 to 70 months, were examined. Mean number of syllables, total number of clauses, utterance complexity (i.e., containing no clauses, simple clauses, or subordinate and/or conjoined clauses), and grammatical correctness (i.e., the presence or absence of morphological and syntactical errors) in stuttered and fluent utterances were compared. Results: Findings revealed that stuttered utterances in Spanish tended to be longer and more often grammatically incorrect, and contain more clauses, including more subordinate and/or conjoined clauses. However, when controlling for the interrelatedness of syllable number and clause number and complexity, only utterance length and grammatical incorrectness were significant predictors of stuttering in the spontaneous speech of these Spanish-speaking children. Use of complex utterances did not appear to contribute to the prediction of stuttering when controlling for utterance length. Conclusions: Results from the present study were consistent with many earlier reports of English-speaking children. Both length and grammatical factors appear to affect stuttering in Spanish-speaking children. Grammatical errors, however, served as the greatest predictor of stuttering.Communication Sciences and Disorder
Seasonality of aerosol optical properties in the Arctic
Given the sensitivity of the Arctic climate to short-lived climate forcers,
long-term in situ surface measurements of aerosol parameters are useful in
gaining insight into the magnitude and variability of these climate forcings.
Seasonality of aerosol optical properties â including the aerosol
light-scattering coefficient, absorption coefficient, single-scattering
albedo, scattering Ă
ngstrĂśm exponent, and asymmetry parameter â are
presented for six monitoring sites throughout the Arctic: Alert, Canada;
Barrow, USA; Pallas, Finland; Summit, Greenland; Tiksi, Russia; and Zeppelin
Mountain, Ny-Ă
lesund, Svalbard, Norway. Results show annual variability
in all parameters, though the seasonality of each aerosol optical property
varies from site to site. There is a large diversity in magnitude and
variability of scattering coefficient at all sites, reflecting differences in
aerosol source, transport, and removal at different locations throughout the
Arctic. Of the Arctic sites, the highest annual mean scattering coefficient
is measured at Tiksi (12.47 Mmâ1), and the lowest annual mean
scattering coefficient is measured at Summit (1.74 Mmâ1). At most
sites, aerosol absorption peaks in the winter and spring, and has a minimum
throughout the Arctic in the summer, indicative of the Arctic haze
phenomenon; however, nuanced variations in seasonalities suggest that this
phenomenon is not identically observed in all regions of the Arctic. The
highest annual mean absorption coefficient is measured at Pallas
(0.48 Mmâ1), and Summit has the lowest annual mean absorption
coefficient (0.12 Mmâ1). At the Arctic monitoring stations analyzed
here, mean annual single-scattering albedo ranges from 0.909 (at Pallas) to
0.960 (at Barrow), the mean annual scattering Ă
ngstrĂśm exponent
ranges from 1.04 (at Barrow) to 1.80 (at Summit), and the mean asymmetry
parameter ranges from 0.57 (at Alert) to 0.75 (at Summit). Systematic
variability of aerosol optical properties in the Arctic supports the notion
that the sites presented here measure a variety of aerosol populations, which
also experience different removal mechanisms. A robust conclusion from the
seasonal cycles presented is that the Arctic cannot be treated as one common
and uniform environment but rather is a region with ample spatiotemporal
variability in aerosols. This notion is important in considering the design
or aerosol monitoring networks in the region and is important for informing
climate models to better represent short-lived aerosol climate forcers in
order to yield more accurate climate predictions for the Arctic.</p
Advanced nursing practice and research contributions to precision medicine
Genomic discoveries in the era of precision medicine hold the promise for tailoring healthcare, symptom management, and research efforts including targeting rare and common diseases through the identification and implementation of genomic-based risk assessment, treatment, and management. However, the translation of these discoveries into tangible benefits for the health of individuals, families, and the public is evolving.; In this article, members of the Genetics Expert Panel identify opportunities for action to increase advanced practice nursing and research contributions toward improving genomic health for all individuals and populations.; Identified opportunities are within the areas of: bolstering genomic focused advanced practice registered nurse practice, research and education efforts; deriving new knowledge about disease biology, risk assessment, treatment efficacy, drug safety and self-management; improving resources and systems that combine genomic information with other healthcare data; and advocating for patient and family benefits and equitable access to genomic healthcare resources
The Use of Technology to Support Precision Health in Nursing Science
PurposeThis article outlines how current nursing research can utilize technology to advance symptom and selfâmanagement science for precision health and provides a roadmap for the development and use of technologies designed for this purpose.ApproachAt the 2018 annual conference of the National Institute of Nursing Research (NINR) Research Centers, nursing and interdisciplinary scientists discussed the use of technology to support precision health in nursing research projects and programs of study. Key themes derived from the presentations and discussion were summarized to create a proposed roadmap for advancement of technologies to support health and wellâbeing.ConclusionsTechnology to support precision health must be centered on the user and designed to be desirable, feasible, and viable. The proposed roadmap is composed of five iterative steps for the development, testing, and implementation of technologyâbased/enhanced selfâmanagement interventions. These steps are (a) contextual inquiry, focused on the relationships among humans, and the tools and equipment used in dayâtoâday life; (b) value specification, translating endâuser values into endâuser requirements; (c) design, verifying that the technology/device can be created and developing the prototype(s); (d) operationalization, testing the intervention in a realâworld setting; and (e) summative evaluation, collecting and analyzing viability metrics, including process data, to evaluate whether the technology and the intervention have the desired effect.Clinical RelevanceInterventions using technology are increasingly popular in precision health. Use of a standard multistep process for the development and testing of technology is essential.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151985/1/jnu12518.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151985/2/jnu12518_am.pd
Biomarkers as Common Data Elements for Symptom and Selfâ Management Science
PurposeBiomarkers as common data elements (CDEs) are important for the characterization of biobehavioral symptoms given that once a biologic moderator or mediator is identified, biologically based strategies can be investigated for treatment efforts. Just as a symptom inventory reflects a symptom experience, a biomarker is an indicator of the symptom, though not the symptom per se. The purposes of this position paper are to (a) identify a â minimum setâ of biomarkers for consideration as CDEs in symptom and selfâ management science, specifically biochemical biomarkers; (b) evaluate the benefits and limitations of such a limited array of biomarkers with implications for symptom science; (c) propose a strategy for the collection of the endorsed minimum set of biologic samples to be employed as CDEs for symptom science; and (d) conceptualize this minimum set of biomarkers consistent with National Institute of Nursing Research (NINR) symptoms of fatigue, depression, cognition, pain, and sleep disturbance.Design and MethodsFrom May 2016 through January 2017, a working group consisting of a subset of the Directors of the NINR Centers of Excellence funded by P20 or P30 mechanisms and NINR staff met bimonthly via telephone to develop this position paper suggesting the addition of biomarkers as CDEs. The full group of Directors reviewed drafts, provided critiques and suggestions, recommended the minimum set of biomarkers, and approved the completed document. Best practices for selecting, identifying, and using biological CDEs as well as challenges to the use of biological CDEs for symptom and selfâ management science are described. Current platforms for sample outcome sharing are presented. Finally, biological CDEs for symptom and selfâ management science are proposed along with implications for future research and use of CDEs in these areas.FindingsThe recommended minimum set of biomarker CDEs include proâ and antiâ inflammatory cytokines, a hypothalamicâ pituitaryâ adrenal axis marker, cortisol, the neuropeptide brainâ derived neurotrophic factor, and DNA polymorphisms.ConclusionsIt is anticipated that this minimum set of biomarker CDEs will be refined as knowledge regarding biologic mechanisms underlying symptom and selfâ management science further develop. The incorporation of biological CDEs may provide insights into mechanisms of symptoms, effectiveness of proposed interventions, and applicability of chosen theoretical frameworks. Similarly, as for the previously suggested NINR CDEs for behavioral symptoms and selfâ management of chronic conditions, biological CDEs offer the potential for collaborative efforts that will strengthen symptom and selfâ management science.Clinical RelevanceThe use of biomarker CDEs in biobehavioral symptoms research will facilitate the reproducibility and generalizability of research findings and benefit symptom and selfâ management science.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143764/1/jnu12378.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143764/2/jnu12378_am.pd
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